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https://paperswithcode.com/paper/minibatch-gibbs-sampling-on-large-graphical | 1806.06086 | null | null | Minibatch Gibbs Sampling on Large Graphical Models | Gibbs sampling is the de facto Markov chain Monte Carlo method used for
inference and learning on large scale graphical models. For complicated factor
graphs with lots of factors, the performance of Gibbs sampling can be limited
by the computational cost of executing a single update step of the Markov
chain. This cost ... | null | http://arxiv.org/abs/1806.06086v1 | http://arxiv.org/pdf/1806.06086v1.pdf | ICML 2018 7 | [
"Christopher De Sa",
"Vincent Chen",
"Wing Wong"
] | [] | 2018-06-15T00:00:00 | https://icml.cc/Conferences/2018/Schedule?showEvent=2383 | http://proceedings.mlr.press/v80/desa18a/desa18a.pdf | minibatch-gibbs-sampling-on-large-graphical-1 | null | [] |
https://paperswithcode.com/paper/detecting-dead-weights-and-units-in-neural | 1806.06068 | null | null | Detecting Dead Weights and Units in Neural Networks | Deep Neural Networks are highly over-parameterized and the size of the neural
networks can be reduced significantly after training without any decrease in
performance. One can clearly see this phenomenon in a wide range of
architectures trained for various problems. Weight/channel pruning,
distillation, quantization, m... | null | http://arxiv.org/abs/1806.06068v1 | http://arxiv.org/pdf/1806.06068v1.pdf | null | [
"Utku Evci"
] | [
"Quantization"
] | 2018-06-15T00:00:00 | null | null | null | null | [
{
"code_snippet_url": null,
"description": "",
"full_name": "Pruning",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "",
"name": "Model Compression",
"parent": null
},
"name": "Pruning",
"source_title": "Pruning Filters for Ef... |
https://paperswithcode.com/paper/classification-with-fairness-constraints-a | 1806.06055 | null | null | Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees | Developing classification algorithms that are fair with respect to sensitive attributes of the data has become an important problem due to the growing deployment of classification algorithms in various social contexts. Several recent works have focused on fairness with respect to a specific metric, modeled the correspo... | The main contribution of this paper is a new meta-algorithm for classification that takes as input a large class of fairness constraints, with respect to multiple non-disjoint sensitive attributes, and which comes with provable guarantees. | https://arxiv.org/abs/1806.06055v3 | https://arxiv.org/pdf/1806.06055v3.pdf | null | [
"L. Elisa Celis",
"Lingxiao Huang",
"Vijay Keswani",
"Nisheeth K. Vishnoi"
] | [
"Classification",
"Fairness",
"General Classification"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/an-online-prediction-algorithm-for | 1806.06720 | null | null | An Online Prediction Algorithm for Reinforcement Learning with Linear Function Approximation using Cross Entropy Method | In this paper, we provide two new stable online algorithms for the problem of
prediction in reinforcement learning, \emph{i.e.}, estimating the value
function of a model-free Markov reward process using the linear function
approximation architecture and with memory and computation costs scaling
quadratically in the siz... | null | http://arxiv.org/abs/1806.06720v1 | http://arxiv.org/pdf/1806.06720v1.pdf | null | [
"Ajin George Joseph",
"Shalabh Bhatnagar"
] | [
"Computational Efficiency",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/deep-lip-reading-a-comparison-of-models-and | 1806.06053 | null | null | Deep Lip Reading: a comparison of models and an online application | The goal of this paper is to develop state-of-the-art models for lip reading
-- visual speech recognition. We develop three architectures and compare their
accuracy and training times: (i) a recurrent model using LSTMs; (ii) a fully
convolutional model; and (iii) the recently proposed transformer model. The
recurrent a... | null | http://arxiv.org/abs/1806.06053v1 | http://arxiv.org/pdf/1806.06053v1.pdf | null | [
"Triantafyllos Afouras",
"Joon Son Chung",
"Andrew Zisserman"
] | [
"Language Modeling",
"Language Modelling",
"Lip Reading",
"speech-recognition",
"Speech Recognition",
"Visual Speech Recognition"
] | 2018-06-15T00:00:00 | null | null | null | null | [
{
"code_snippet_url": null,
"description": "A **Linear Layer** is a projection $\\mathbf{XW + b}$.",
"full_name": "Linear Layer",
"introduced_year": 2000,
"main_collection": {
"area": "General",
"description": "**Feedforward Networks** are a type of neural network architecture which ... |
https://paperswithcode.com/paper/versatile-auxiliary-classifier-with-1 | 1805.00316 | null | null | Versatile Auxiliary Classifier with Generative Adversarial Network (VAC+GAN) | One of the most interesting challenges in Artificial Intelligence is to train
conditional generators which are able to provide labeled adversarial samples
drawn from a specific distribution. In this work, a new framework is presented
to train a deep conditional generator by placing a classifier in parallel with
the dis... | null | http://arxiv.org/abs/1805.00316v3 | http://arxiv.org/pdf/1805.00316v3.pdf | null | [
"Shabab Bazrafkan",
"Hossein Javidnia",
"Peter Corcoran"
] | [
"General Classification",
"Generative Adversarial Network"
] | 2018-05-01T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/computationally-efficient-estimation-of-the | 1806.06047 | null | null | Computationally Efficient Estimation of the Spectral Gap of a Markov Chain | We consider the problem of estimating from sample paths the absolute spectral
gap $\gamma_*$ of a reversible, irreducible and aperiodic Markov chain
$(X_t)_{t \in \mathbb{N}}$ over a finite state space $\Omega$. We propose the
${\tt UCPI}$ (Upper Confidence Power Iteration) algorithm for this problem, a
low-complexity ... | null | http://arxiv.org/abs/1806.06047v2 | http://arxiv.org/pdf/1806.06047v2.pdf | null | [
"Richard Combes",
"Mikael Touati"
] | [] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/high-quality-prediction-intervals-for-deep | 1802.07167 | null | null | High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach | This paper considers the generation of prediction intervals (PIs) by neural
networks for quantifying uncertainty in regression tasks. It is axiomatic that
high-quality PIs should be as narrow as possible, whilst capturing a specified
portion of data. We derive a loss function directly from this axiom that
requires no d... | This paper considers the generation of prediction intervals (PIs) by neural networks for quantifying uncertainty in regression tasks. | http://arxiv.org/abs/1802.07167v3 | http://arxiv.org/pdf/1802.07167v3.pdf | ICML 2018 7 | [
"Tim Pearce",
"Mohamed Zaki",
"Alexandra Brintrup",
"Andy Neely"
] | [
"Form",
"Prediction Intervals",
"regression",
"Uncertainty Quantification"
] | 2018-02-20T00:00:00 | https://icml.cc/Conferences/2018/Schedule?showEvent=2188 | http://proceedings.mlr.press/v80/pearce18a/pearce18a.pdf | high-quality-prediction-intervals-for-deep-1 | null | [] |
https://paperswithcode.com/paper/bayesian-inference-with-anchored-ensembles-of | 1805.11324 | null | null | Bayesian Inference with Anchored Ensembles of Neural Networks, and Application to Exploration in Reinforcement Learning | The use of ensembles of neural networks (NNs) for the quantification of
predictive uncertainty is widespread. However, the current justification is
intuitive rather than analytical. This work proposes one minor modification to
the normal ensembling methodology, which we prove allows the ensemble to
perform Bayesian inf... | The use of ensembles of neural networks (NNs) for the quantification of predictive uncertainty is widespread. | http://arxiv.org/abs/1805.11324v3 | http://arxiv.org/pdf/1805.11324v3.pdf | null | [
"Tim Pearce",
"Nicolas Anastassacos",
"Mohamed Zaki",
"Andy Neely"
] | [
"Bayesian Inference",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-05-29T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/three-factors-influencing-minima-in-sgd | 1711.04623 | null | rJma2bZCW | Three Factors Influencing Minima in SGD | We investigate the dynamical and convergent properties of stochastic gradient
descent (SGD) applied to Deep Neural Networks (DNNs). Characterizing the
relation between learning rate, batch size and the properties of the final
minima, such as width or generalization, remains an open question. In order to
tackle this pro... | null | http://arxiv.org/abs/1711.04623v3 | http://arxiv.org/pdf/1711.04623v3.pdf | ICLR 2018 1 | [
"Stanisław Jastrzębski",
"Zachary Kenton",
"Devansh Arpit",
"Nicolas Ballas",
"Asja Fischer",
"Yoshua Bengio",
"Amos Storkey"
] | [
"Memorization",
"Open-Ended Question Answering"
] | 2017-11-13T00:00:00 | https://openreview.net/forum?id=rJma2bZCW | https://openreview.net/pdf?id=rJma2bZCW | three-factors-influencing-minima-in-sgd-1 | null | [
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/4e0ac120e9a8b096069c2f892488d630a5c8f358/torch/optim/sgd.py#L97-L112",
"description": "**Stochastic Gradient Descent** is an iterative optimization technique that uses minibatches of data to form an expectation of the gradient, rather than the f... |
https://paperswithcode.com/paper/the-toybox-dataset-of-egocentric-visual | 1806.06034 | null | null | The Toybox Dataset of Egocentric Visual Object Transformations | In object recognition research, many commonly used datasets (e.g., ImageNet
and similar) contain relatively sparse distributions of object instances and
views, e.g., one might see a thousand different pictures of a thousand
different giraffes, mostly taken from a few conventionally photographed angles.
These distributi... | null | http://arxiv.org/abs/1806.06034v3 | http://arxiv.org/pdf/1806.06034v3.pdf | null | [
"Xiaohan Wang",
"Tengyu Ma",
"James Ainooson",
"Seunghwan Cha",
"Xiaotian Wang",
"Azhar Molla",
"Maithilee Kunda"
] | [
"Object",
"Object Recognition",
"Translation"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/latent-space-physics-towards-learning-the | 1802.10123 | null | null | Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow | We propose a method for the data-driven inference of temporal evolutions of
physical functions with deep learning. More specifically, we target fluid
flows, i.e. Navier-Stokes problems, and we propose a novel LSTM-based approach
to predict the changes of pressure fields over time. The central challenge in
this context ... | We propose a method for the data-driven inference of temporal evolutions of physical functions with deep learning. | http://arxiv.org/abs/1802.10123v3 | http://arxiv.org/pdf/1802.10123v3.pdf | null | [
"Steffen Wiewel",
"Moritz Becher",
"Nils Thuerey"
] | [
"Dimensionality Reduction"
] | 2018-02-27T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/one-shot-unsupervised-cross-domain | 1806.06029 | null | null | One-Shot Unsupervised Cross Domain Translation | Given a single image x from domain A and a set of images from domain B, our
task is to generate the analogous of x in B. We argue that this task could be a
key AI capability that underlines the ability of cognitive agents to act in the
world and present empirical evidence that the existing unsupervised domain
translati... | Given a single image x from domain A and a set of images from domain B, our task is to generate the analogous of x in B. | http://arxiv.org/abs/1806.06029v2 | http://arxiv.org/pdf/1806.06029v2.pdf | NeurIPS 2018 12 | [
"Sagie Benaim",
"Lior Wolf"
] | [
"Translation",
"Unsupervised Image-To-Image Translation",
"Zero-Shot Learning"
] | 2018-06-15T00:00:00 | http://papers.nips.cc/paper/7480-one-shot-unsupervised-cross-domain-translation | http://papers.nips.cc/paper/7480-one-shot-unsupervised-cross-domain-translation.pdf | one-shot-unsupervised-cross-domain-1 | null | [
{
"code_snippet_url": "",
"description": "In today’s digital age, Solana has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing wit... |
https://paperswithcode.com/paper/variational-attention-for-sequence-to | 1712.08207 | null | null | Variational Attention for Sequence-to-Sequence Models | The variational encoder-decoder (VED) encodes source information as a set of
random variables using a neural network, which in turn is decoded into target
data using another neural network. In natural language processing,
sequence-to-sequence (Seq2Seq) models typically serve as encoder-decoder
networks. When combined w... | The variational encoder-decoder (VED) encodes source information as a set of random variables using a neural network, which in turn is decoded into target data using another neural network. | http://arxiv.org/abs/1712.08207v3 | http://arxiv.org/pdf/1712.08207v3.pdf | COLING 2018 8 | [
"Hareesh Bahuleyan",
"Lili Mou",
"Olga Vechtomova",
"Pascal Poupart"
] | [
"Decoder",
"Diversity"
] | 2017-12-21T00:00:00 | https://aclanthology.org/C18-1142 | https://aclanthology.org/C18-1142.pdf | variational-attention-for-sequence-to-2 | null | [] |
https://paperswithcode.com/paper/low-shot-learning-with-large-scale-diffusion | 1706.02332 | null | null | Low-shot learning with large-scale diffusion | This paper considers the problem of inferring image labels from images when
only a few annotated examples are available at training time. This setup is
often referred to as low-shot learning, where a standard approach is to
re-train the last few layers of a convolutional neural network learned on
separate classes for w... | This paper considers the problem of inferring image labels from images when only a few annotated examples are available at training time. | http://arxiv.org/abs/1706.02332v3 | http://arxiv.org/pdf/1706.02332v3.pdf | CVPR 2018 6 | [
"Matthijs Douze",
"Arthur Szlam",
"Bharath Hariharan",
"Hervé Jégou"
] | [
"Few-Shot Image Classification",
"graph construction"
] | 2017-06-07T00:00:00 | http://openaccess.thecvf.com/content_cvpr_2018/html/Douze_Low-Shot_Learning_With_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Douze_Low-Shot_Learning_With_CVPR_2018_paper.pdf | low-shot-learning-with-large-scale-diffusion-1 | null | [] |
https://paperswithcode.com/paper/homonym-detection-in-curated-bibliographies | 1806.06017 | null | null | Homonym Detection in Curated Bibliographies: Learning from dblp's Experience (full version) | Identifying (and fixing) homonymous and synonymous author profiles is one of
the major tasks of curating personalized bibliographic metadata repositories
like the dblp computer science bibliography. In this paper, we present and
evaluate a machine learning approach to identify homonymous author
bibliographies using a s... | null | http://arxiv.org/abs/1806.06017v1 | http://arxiv.org/pdf/1806.06017v1.pdf | null | [
"Marcel R. Ackermann",
"Florian Reitz"
] | [
"BIG-bench Machine Learning"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/detecting-abnormal-events-in-video-using | 1801.05030 | null | null | Detecting abnormal events in video using Narrowed Normality Clusters | We formulate the abnormal event detection problem as an outlier detection
task and we propose a two-stage algorithm based on k-means clustering and
one-class Support Vector Machines (SVM) to eliminate outliers. In the feature
extraction stage, we propose to augment spatio-temporal cubes with deep
appearance features ex... | null | http://arxiv.org/abs/1801.05030v4 | http://arxiv.org/pdf/1801.05030v4.pdf | null | [
"Radu Tudor Ionescu",
"Sorina Smeureanu",
"Marius Popescu",
"Bogdan Alexe"
] | [
"Anomaly Detection",
"Clustering",
"CPU",
"Event Detection",
"Outlier Detection"
] | 2018-01-12T00:00:00 | null | null | null | null | [
{
"code_snippet_url": "",
"description": "A **Support Vector Machine**, or **SVM**, is a non-parametric supervised learning model. For non-linear classification and regression, they utilise the kernel trick to map inputs to high-dimensional feature spaces. SVMs construct a hyper-plane or set of hyper-planes... |
https://paperswithcode.com/paper/real-time-deep-learning-method-for-abandoned | 1803.01160 | null | null | Real-Time Deep Learning Method for Abandoned Luggage Detection in Video | Recent terrorist attacks in major cities around the world have brought many
casualties among innocent citizens. One potential threat is represented by
abandoned luggage items (that could contain bombs or biological warfare) in
public areas. In this paper, we describe an approach for real-time automatic
detection of aba... | null | http://arxiv.org/abs/1803.01160v3 | http://arxiv.org/pdf/1803.01160v3.pdf | null | [
"Sorina Smeureanu",
"Radu Tudor Ionescu"
] | [
"Deep Learning",
"Motion Estimation",
"object-detection",
"Object Detection"
] | 2018-03-03T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/optimizing-the-trade-off-between-single-stage | 1803.08707 | null | null | Optimizing the Trade-off between Single-Stage and Two-Stage Object Detectors using Image Difficulty Prediction | There are mainly two types of state-of-the-art object detectors. On one hand,
we have two-stage detectors, such as Faster R-CNN (Region-based Convolutional
Neural Networks) or Mask R-CNN, that (i) use a Region Proposal Network to
generate regions of interests in the first stage and (ii) send the region
proposals down t... | null | http://arxiv.org/abs/1803.08707v3 | http://arxiv.org/pdf/1803.08707v3.pdf | null | [
"Petru Soviany",
"Radu Tudor Ionescu"
] | [
"Object",
"object-detection",
"Object Detection",
"Region Proposal",
"regression"
] | 2018-03-23T00:00:00 | null | null | null | null | [
{
"code_snippet_url": "https://github.com/lorenzopapa5/SPEED",
"description": "The monocular depth estimation (MDE) is the task of estimating depth from a single frame. This information is an essential knowledge in many computer vision tasks such as scene understanding and visual odometry, which are key com... |
https://paperswithcode.com/paper/partially-supervised-image-captioning | 1806.06004 | null | null | Partially-Supervised Image Captioning | Image captioning models are becoming increasingly successful at describing
the content of images in restricted domains. However, if these models are to
function in the wild - for example, as assistants for people with impaired
vision - a much larger number and variety of visual concepts must be
understood. To address t... | null | http://arxiv.org/abs/1806.06004v2 | http://arxiv.org/pdf/1806.06004v2.pdf | NeurIPS 2018 12 | [
"Peter Anderson",
"Stephen Gould",
"Mark Johnson"
] | [
"Image Captioning",
"Object",
"object-detection",
"Object Detection",
"Sentence"
] | 2018-06-15T00:00:00 | http://papers.nips.cc/paper/7458-partially-supervised-image-captioning | http://papers.nips.cc/paper/7458-partially-supervised-image-captioning.pdf | partially-supervised-image-captioning-1 | null | [] |
https://paperswithcode.com/paper/on-machine-learning-and-structure-for-mobile | 1806.06003 | null | null | On Machine Learning and Structure for Mobile Robots | Due to recent advances - compute, data, models - the role of learning in
autonomous systems has expanded significantly, rendering new applications
possible for the first time. While some of the most significant benefits are
obtained in the perception modules of the software stack, other aspects
continue to rely on know... | null | http://arxiv.org/abs/1806.06003v1 | http://arxiv.org/pdf/1806.06003v1.pdf | null | [
"Markus Wulfmeier"
] | [
"BIG-bench Machine Learning"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/adapting-neural-text-classification-for | 1806.01742 | null | null | Adapting Neural Text Classification for Improved Software Categorization | Software Categorization is the task of organizing software into groups that
broadly describe the behavior of the software, such as "editors" or "science."
Categorization plays an important role in several maintenance tasks, such as
repository navigation and feature elicitation. Current approaches attempt to
cast the pr... | Software Categorization is the task of organizing software into groups that broadly describe the behavior of the software, such as "editors" or "science." | http://arxiv.org/abs/1806.01742v2 | http://arxiv.org/pdf/1806.01742v2.pdf | null | [
"Alexander LeClair",
"Zachary Eberhart",
"Collin McMillan"
] | [
"Classification",
"General Classification",
"text-classification",
"Text Classification"
] | 2018-06-05T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/a-dataset-for-building-code-mixed-goal | 1806.05997 | null | null | A Dataset for Building Code-Mixed Goal Oriented Conversation Systems | There is an increasing demand for goal-oriented conversation systems which
can assist users in various day-to-day activities such as booking tickets,
restaurant reservations, shopping, etc. Most of the existing datasets for
building such conversation systems focus on monolingual conversations and there
is hardly any wo... | ("Can you help me in booking a table at this restaurant?"). | http://arxiv.org/abs/1806.05997v1 | http://arxiv.org/pdf/1806.05997v1.pdf | COLING 2018 8 | [
"Suman Banerjee",
"Nikita Moghe",
"Siddhartha Arora",
"Mitesh M. Khapra"
] | [
"Goal-Oriented Dialog"
] | 2018-06-15T00:00:00 | https://aclanthology.org/C18-1319 | https://aclanthology.org/C18-1319.pdf | a-dataset-for-building-code-mixed-goal-2 | null | [] |
https://paperswithcode.com/paper/ego-lane-analysis-system-elas-dataset-and | 1806.05984 | null | null | Ego-Lane Analysis System (ELAS): Dataset and Algorithms | Decreasing costs of vision sensors and advances in embedded hardware boosted
lane related research detection, estimation, and tracking in the past two
decades. The interest in this topic has increased even more with the demand for
advanced driver assistance systems (ADAS) and self-driving cars. Although
extensively stu... | null | http://arxiv.org/abs/1806.05984v1 | http://arxiv.org/pdf/1806.05984v1.pdf | null | [
"Rodrigo F. Berriel",
"Edilson de Aguiar",
"Alberto F. de Souza",
"Thiago Oliveira-Santos"
] | [
"Change Detection",
"General Classification",
"Self-Driving Cars"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/bayesian-convolutional-neural-networks-with-1 | 1806.05978 | null | null | Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference | We introduce a novel uncertainty estimation for classification tasks for Bayesian convolutional neural networks with variational inference. By normalizing the output of a Softplus function in the final layer, we estimate aleatoric and epistemic uncertainty in a coherent manner. The intractable posterior probability dis... | On multiple datasets in supervised learning settings (MNIST, CIFAR-10, CIFAR-100), this variational inference method achieves performances equivalent to frequentist inference in identical architectures, while the two desiderata, a measure for uncertainty and regularization are incorporated naturally. | https://arxiv.org/abs/1806.05978v6 | https://arxiv.org/pdf/1806.05978v6.pdf | null | [
"Kumar Shridhar",
"Felix Laumann",
"Marcus Liwicki"
] | [
"Bayesian Inference",
"General Classification",
"Variational Inference"
] | 2018-06-15T00:00:00 | null | null | null | null | [
{
"code_snippet_url": "",
"description": "How Do I File a Claim with Expedia?\r\nCall **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Fast Help & Exclusive Travel Discounts!Need to file a claim with Expedia? Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** n... |
https://paperswithcode.com/paper/supervised-learning-with-generalized-tensor | 1806.05964 | null | null | From probabilistic graphical models to generalized tensor networks for supervised learning | Tensor networks have found a wide use in a variety of applications in physics and computer science, recently leading to both theoretical insights as well as practical algorithms in machine learning. In this work we explore the connection between tensor networks and probabilistic graphical models, and show that it motiv... | null | https://arxiv.org/abs/1806.05964v2 | https://arxiv.org/pdf/1806.05964v2.pdf | null | [
"Ivan Glasser",
"Nicola Pancotti",
"J. Ignacio Cirac"
] | [
"BIG-bench Machine Learning",
"Quantum Machine Learning",
"Tensor Networks"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/techniques-for-visualizing-lstms-applied-to | 1705.08153 | null | null | Techniques for visualizing LSTMs applied to electrocardiograms | This paper explores four different visualization techniques for long
short-term memory (LSTM) networks applied to continuous-valued time series. On
the datasets analysed, we find that the best visualization technique is to
learn an input deletion mask that optimally reduces the true class score. With
a specific focus o... | null | http://arxiv.org/abs/1705.08153v3 | http://arxiv.org/pdf/1705.08153v3.pdf | null | [
"Jos van der Westhuizen",
"Joan Lasenby"
] | [
"Time Series",
"Time Series Analysis"
] | 2017-05-23T00:00:00 | null | null | null | null | [
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\... |
https://paperswithcode.com/paper/deep-temporal-lstm-for-daily-living-action | 1802.00421 | null | null | Deep-Temporal LSTM for Daily Living Action Recognition | In this paper, we propose to improve the traditional use of RNNs by employing
a many to many model for video classification. We analyze the importance of
modeling spatial layout and temporal encoding for daily living action
recognition. Many RGB methods focus only on short term temporal information
obtained from optica... | null | http://arxiv.org/abs/1802.00421v2 | http://arxiv.org/pdf/1802.00421v2.pdf | null | [
"Srijan Das",
"Michal Koperski",
"Francois Bremond",
"Gianpiero Francesca"
] | [
"Action Recognition",
"General Classification",
"Optical Flow Estimation",
"Temporal Action Localization",
"Video Classification"
] | 2018-02-01T00:00:00 | null | null | null | null | [
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\... |
https://paperswithcode.com/paper/controllable-semantic-image-inpainting | 1806.05953 | null | null | Controllable Semantic Image Inpainting | We develop a method for user-controllable semantic image inpainting: Given an
arbitrary set of observed pixels, the unobserved pixels can be imputed in a
user-controllable range of possibilities, each of which is semantically
coherent and locally consistent with the observed pixels. We achieve this using
a deep generat... | null | http://arxiv.org/abs/1806.05953v1 | http://arxiv.org/pdf/1806.05953v1.pdf | null | [
"Jin Xu",
"Yee Whye Teh"
] | [
"Image Inpainting"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/amortized-context-vector-inference-for | 1805.09039 | null | SygONjRqKm | Amortized Context Vector Inference for Sequence-to-Sequence Networks | Neural attention (NA) has become a key component of sequence-to-sequence
models that yield state-of-the-art performance in as hard tasks as abstractive
document summarization (ADS) and video captioning (VC). NA mechanisms perform
inference of context vectors; these constitute weighted sums of deterministic
input sequen... | null | http://arxiv.org/abs/1805.09039v9 | http://arxiv.org/pdf/1805.09039v9.pdf | null | [
"Kyriacos Tolias",
"Ioannis Kourouklides",
"Sotirios Chatzis"
] | [
"Document Summarization",
"Variational Inference",
"Video Captioning"
] | 2018-05-23T00:00:00 | https://openreview.net/forum?id=SygONjRqKm | https://openreview.net/pdf?id=SygONjRqKm | null | null | [] |
https://paperswithcode.com/paper/a-challenge-set-for-french-english-machine | 1806.02725 | null | null | A Challenge Set for French --> English Machine Translation | We present a challenge set for French --> English machine translation based
on the approach introduced in Isabelle, Cherry and Foster (EMNLP 2017). Such
challenge sets are made up of sentences that are expected to be relatively
difficult for machines to translate correctly because their most
straightforward translation... | null | http://arxiv.org/abs/1806.02725v2 | http://arxiv.org/pdf/1806.02725v2.pdf | null | [
"Pierre Isabelle",
"Roland Kuhn"
] | [
"Machine Translation",
"Translation"
] | 2018-06-07T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/discovering-user-groups-for-natural-language | 1806.05947 | null | null | Discovering User Groups for Natural Language Generation | We present a model which predicts how individual users of a dialog system
understand and produce utterances based on user groups. In contrast to previous
work, these user groups are not specified beforehand, but learned in training.
We evaluate on two referring expression (RE) generation tasks; our experiments
show tha... | null | http://arxiv.org/abs/1806.05947v1 | http://arxiv.org/pdf/1806.05947v1.pdf | WS 2018 7 | [
"Nikos Engonopoulos",
"Christoph Teichmann",
"Alexander Koller"
] | [
"Referring Expression",
"Text Generation"
] | 2018-06-15T00:00:00 | https://aclanthology.org/W18-5018 | https://aclanthology.org/W18-5018.pdf | discovering-user-groups-for-natural-language-1 | null | [] |
https://paperswithcode.com/paper/efficient-nearest-neighbors-search-for-large | 1806.05946 | null | null | Efficient Nearest Neighbors Search for Large-Scale Landmark Recognition | The problem of landmark recognition has achieved excellent results in
small-scale datasets. When dealing with large-scale retrieval, issues that were
irrelevant with small amount of data, quickly become fundamental for an
efficient retrieval phase. In particular, computational time needs to be kept
as low as possible, ... | It allows to drastically reduce the query time and outperforms the accuracy results compared to the state-of-the-art methods for large-scale landmark recognition. | http://arxiv.org/abs/1806.05946v1 | http://arxiv.org/pdf/1806.05946v1.pdf | null | [
"Federico Magliani",
"Tomaso Fontanini",
"Andrea Prati"
] | [
"Landmark Recognition",
"Retrieval"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/repmet-representative-based-metric-learning | 1806.04728 | null | null | RepMet: Representative-based metric learning for classification and one-shot object detection | Distance metric learning (DML) has been successfully applied to object
classification, both in the standard regime of rich training data and in the
few-shot scenario, where each category is represented by only a few examples.
In this work, we propose a new method for DML that simultaneously learns the
backbone network ... | Distance metric learning (DML) has been successfully applied to object classification, both in the standard regime of rich training data and in the few-shot scenario, where each category is represented by only a few examples. | http://arxiv.org/abs/1806.04728v3 | http://arxiv.org/pdf/1806.04728v3.pdf | null | [
"Leonid Karlinsky",
"Joseph Shtok",
"Sivan Harary",
"Eli Schwartz",
"Amit Aides",
"Rogerio Feris",
"Raja Giryes",
"Alex M. Bronstein"
] | [
"Classification",
"Few-Shot Object Detection",
"General Classification",
"Metric Learning",
"Object",
"object-detection",
"Object Detection",
"One-Shot Object Detection"
] | 2018-06-12T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/query-k-means-clustering-and-the-double-dixie | 1806.05938 | null | null | Query K-means Clustering and the Double Dixie Cup Problem | We consider the problem of approximate $K$-means clustering with outliers and
side information provided by same-cluster queries and possibly noisy answers.
Our solution shows that, under some mild assumptions on the smallest cluster
size, one can obtain an $(1+\epsilon)$-approximation for the optimal potential
with pro... | null | http://arxiv.org/abs/1806.05938v2 | http://arxiv.org/pdf/1806.05938v2.pdf | NeurIPS 2018 12 | [
"I Chien",
"Chao Pan",
"Olgica Milenkovic"
] | [
"Clustering"
] | 2018-06-15T00:00:00 | http://papers.nips.cc/paper/7899-query-k-means-clustering-and-the-double-dixie-cup-problem | http://papers.nips.cc/paper/7899-query-k-means-clustering-and-the-double-dixie-cup-problem.pdf | query-k-means-clustering-and-the-double-dixie-1 | null | [] |
https://paperswithcode.com/paper/dynamic-weight-alignment-for-temporal | 1712.06530 | null | null | Dynamic Weight Alignment for Temporal Convolutional Neural Networks | In this paper, we propose a method of improving temporal Convolutional Neural
Networks (CNN) by determining the optimal alignment of weights and inputs using
dynamic programming. Conventional CNN convolutions linearly match the shared
weights to a window of the input. However, it is possible that there exists a
more op... | null | http://arxiv.org/abs/1712.06530v6 | http://arxiv.org/pdf/1712.06530v6.pdf | null | [
"Brian Kenji Iwana",
"Seiichi Uchida"
] | [
"Dynamic Time Warping",
"Time Series Analysis"
] | 2017-12-18T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/learning-semantic-sentence-embeddings-using-1 | 1806.00807 | null | null | Learning Semantic Sentence Embeddings using Sequential Pair-wise Discriminator | In this paper, we propose a method for obtaining sentence-level embeddings.
While the problem of securing word-level embeddings is very well studied, we
propose a novel method for obtaining sentence-level embeddings. This is
obtained by a simple method in the context of solving the paraphrase generation
task. If we use... | One way to ensure this is by adding constraints for true paraphrase embeddings to be close and unrelated paraphrase candidate sentence embeddings to be far. | http://arxiv.org/abs/1806.00807v5 | http://arxiv.org/pdf/1806.00807v5.pdf | COLING 2018 8 | [
"Badri N. Patro",
"Vinod K. Kurmi",
"Sandeep Kumar",
"Vinay P. Namboodiri"
] | [
"Decoder",
"Paraphrase Generation",
"Sentence",
"Sentence Embedding",
"Sentence-Embedding",
"Sentence Embeddings",
"Sentiment Analysis"
] | 2018-06-03T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/bayesian-best-arm-identification-for | 1711.06299 | null | null | Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies | Pandemic influenza has the epidemic potential to kill millions of people.
While various preventive measures exist (i.a., vaccination and school
closures), deciding on strategies that lead to their most effective and
efficient use remains challenging. To this end, individual-based
epidemiological models are essential to... | null | http://arxiv.org/abs/1711.06299v2 | http://arxiv.org/pdf/1711.06299v2.pdf | null | [
"Pieter Libin",
"Timothy Verstraeten",
"Diederik M. Roijers",
"Jelena Grujic",
"Kristof Theys",
"Philippe Lemey",
"Ann Nowé"
] | [
"Decision Making",
"Thompson Sampling"
] | 2017-11-16T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/robust-bayesian-model-selection-for-variable | 1806.05924 | null | null | Robust Bayesian Model Selection for Variable Clustering with the Gaussian Graphical Model | Variable clustering is important for explanatory analysis. However, only few
dedicated methods for variable clustering with the Gaussian graphical model
have been proposed. Even more severe, small insignificant partial correlations
due to noise can dramatically change the clustering result when evaluating for
example w... | Even more severe, small insignificant partial correlations due to noise can dramatically change the clustering result when evaluating for example with the Bayesian Information Criteria (BIC). | http://arxiv.org/abs/1806.05924v1 | http://arxiv.org/pdf/1806.05924v1.pdf | null | [
"Daniel Andrade",
"Akiko Takeda",
"Kenji Fukumizu"
] | [
"Clustering",
"model",
"Model Selection"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/online-feature-ranking-for-intrusion | 1803.00530 | null | null | Online Feature Ranking for Intrusion Detection Systems | Many current approaches to the design of intrusion detection systems apply
feature selection in a static, non-adaptive fashion. These methods often
neglect the dynamic nature of network data which requires to use adaptive
feature selection techniques. In this paper, we present a simple technique
based on incremental le... | null | http://arxiv.org/abs/1803.00530v2 | http://arxiv.org/pdf/1803.00530v2.pdf | null | [
"Buse Gul Atli",
"Alexander Jung"
] | [
"feature selection",
"Incremental Learning",
"Intrusion Detection"
] | 2018-03-01T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/an-empirical-analysis-of-the-correlation-of | 1806.05900 | null | null | An Empirical Analysis of the Correlation of Syntax and Prosody | The relation of syntax and prosody (the syntax--prosody interface) has been
an active area of research, mostly in linguistics and typically studied under
controlled conditions. More recently, prosody has also been successfully used
in the data-based training of syntax parsers. However, there is a gap between
the contro... | null | http://arxiv.org/abs/1806.05900v1 | http://arxiv.org/pdf/1806.05900v1.pdf | null | [
"Arne Köhn",
"Timo Baumann",
"Oskar Dörfler"
] | [] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/magix-model-agnostic-globally-interpretable | 1706.07160 | null | null | MAGIX: Model Agnostic Globally Interpretable Explanations | Explaining the behavior of a black box machine learning model at the instance
level is useful for building trust. However, it is also important to understand
how the model behaves globally. Such an understanding provides insight into
both the data on which the model was trained and the patterns that it learned.
We pres... | null | http://arxiv.org/abs/1706.07160v3 | http://arxiv.org/pdf/1706.07160v3.pdf | null | [
"Nikaash Puri",
"Piyush Gupta",
"Pratiksha Agarwal",
"Sukriti Verma",
"Balaji Krishnamurthy"
] | [
"BIG-bench Machine Learning",
"Marketing",
"model"
] | 2017-06-22T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/improving-width-based-planning-with-compact | 1806.05898 | null | null | Improving width-based planning with compact policies | Optimal action selection in decision problems characterized by sparse,
delayed rewards is still an open challenge. For these problems, current deep
reinforcement learning methods require enormous amounts of data to learn
controllers that reach human-level performance. In this work, we propose a
method that interleaves ... | null | http://arxiv.org/abs/1806.05898v1 | http://arxiv.org/pdf/1806.05898v1.pdf | null | [
"Miquel Junyent",
"Anders Jonsson",
"Vicenç Gómez"
] | [
"Atari Games",
"Deep Reinforcement Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-15T00:00:00 | null | null | null | null | [
{
"code_snippet_url": null,
"description": "**A2C**, or **Advantage Actor Critic**, is a synchronous version of the [A3C](https://paperswithcode.com/method/a3c) policy gradient method. As an alternative to the asynchronous implementation of A3C, A2C is a synchronous, deterministic implementation that waits ... |
https://paperswithcode.com/paper/mining-rank-data | 1806.05897 | null | null | Mining Rank Data | The problem of frequent pattern mining has been studied quite extensively for
various types of data, including sets, sequences, and graphs. Somewhat
surprisingly, another important type of data, namely rank data, has received
very little attention in data mining so far. In this paper, we therefore
addresses the problem... | null | http://arxiv.org/abs/1806.05897v1 | http://arxiv.org/pdf/1806.05897v1.pdf | null | [
"Sascha Henzgen",
"Eyke Hüllermeier"
] | [
"Form"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/learning-front-end-filter-bank-parameters | 1806.05892 | null | null | Learning Front-end Filter-bank Parameters using Convolutional Neural Networks for Abnormal Heart Sound Detection | Automatic heart sound abnormality detection can play a vital role in the
early diagnosis of heart diseases, particularly in low-resource settings. The
state-of-the-art algorithms for this task utilize a set of Finite Impulse
Response (FIR) band-pass filters as a front-end followed by a Convolutional
Neural Network (CNN... | In this work, we propound a novel CNN architecture that integrates the front-end bandpass filters within the network using time-convolution (tConv) layers, which enables the FIR filter-bank parameters to become learnable. | http://arxiv.org/abs/1806.05892v1 | http://arxiv.org/pdf/1806.05892v1.pdf | null | [
"Ahmed Imtiaz Humayun",
"Shabnam Ghaffarzadegan",
"Zhe Feng",
"Taufiq Hasan"
] | [
"Anomaly Detection"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/structured-low-rank-matrix-learning | 1704.07352 | null | null | Structured low-rank matrix learning: algorithms and applications | We consider the problem of learning a low-rank matrix, constrained to lie in
a linear subspace, and introduce a novel factorization for modeling such
matrices. A salient feature of the proposed factorization scheme is it
decouples the low-rank and the structural constraints onto separate factors. We
formulate the optim... | null | http://arxiv.org/abs/1704.07352v5 | http://arxiv.org/pdf/1704.07352v5.pdf | null | [
"Pratik Jawanpuria",
"Bamdev Mishra"
] | [
"Matrix Completion",
"Multi-Task Learning"
] | 2017-04-24T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/hierarchical-novelty-detection-for-visual | 1804.00722 | null | null | Hierarchical Novelty Detection for Visual Object Recognition | Deep neural networks have achieved impressive success in large-scale visual
object recognition tasks with a predefined set of classes. However, recognizing
objects of novel classes unseen during training still remains challenging. The
problem of detecting such novel classes has been addressed in the literature,
but mos... | null | http://arxiv.org/abs/1804.00722v2 | http://arxiv.org/pdf/1804.00722v2.pdf | CVPR 2018 6 | [
"Kibok Lee",
"Kimin Lee",
"Kyle Min",
"Yuting Zhang",
"Jinwoo Shin",
"Honglak Lee"
] | [
"Generalized Zero-Shot Learning",
"Novelty Detection",
"Object",
"Object Recognition",
"Zero-Shot Learning"
] | 2018-04-02T00:00:00 | http://openaccess.thecvf.com/content_cvpr_2018/html/Lee_Hierarchical_Novelty_Detection_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Lee_Hierarchical_Novelty_Detection_CVPR_2018_paper.pdf | hierarchical-novelty-detection-for-visual-1 | null | [] |
https://paperswithcode.com/paper/automated-image-data-preprocessing-with-deep | 1806.05886 | null | null | Automated Image Data Preprocessing with Deep Reinforcement Learning | Data preparation, i.e. the process of transforming raw data into a format that can be used for training effective machine learning models, is a tedious and time-consuming task. For image data, preprocessing typically involves a sequence of basic transformations such as cropping, filtering, rotating or flipping images. ... | Data preparation, i. e. the process of transforming raw data into a format that can be used for training effective machine learning models, is a tedious and time-consuming task. | https://arxiv.org/abs/1806.05886v2 | https://arxiv.org/pdf/1806.05886v2.pdf | null | [
"Tran Ngoc Minh",
"Mathieu Sinn",
"Hoang Thanh Lam",
"Martin Wistuba"
] | [
"Deep Reinforcement Learning",
"reinforcement-learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/a-simple-blind-denoising-filter-inspired-by | 1806.05882 | null | null | A simple blind-denoising filter inspired by electrically coupled photoreceptors in the retina | Photoreceptors in the retina are coupled by electrical synapses called "gap
junctions". It has long been established that gap junctions increase the
signal-to-noise ratio of photoreceptors. Inspired by electrically coupled
photoreceptors, we introduced a simple filter, the PR-filter, with only one
variable. On BSD68 da... | null | http://arxiv.org/abs/1806.05882v4 | http://arxiv.org/pdf/1806.05882v4.pdf | null | [
"Yang Yue",
"Liuyuan He",
"Gan He",
"Jian. K. Liu",
"Kai Du",
"Yonghong Tian",
"Tiejun Huang"
] | [
"Denoising",
"SSIM"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/neural-stethoscopes-unifying-analytic | 1806.05502 | null | null | Scrutinizing and De-Biasing Intuitive Physics with Neural Stethoscopes | Visually predicting the stability of block towers is a popular task in the domain of intuitive physics. While previous work focusses on prediction accuracy, a one-dimensional performance measure, we provide a broader analysis of the learned physical understanding of the final model and how the learning process can be g... | null | https://arxiv.org/abs/1806.05502v5 | https://arxiv.org/pdf/1806.05502v5.pdf | null | [
"Fabian B. Fuchs",
"Oliver Groth",
"Adam R. Kosiorek",
"Alex Bewley",
"Markus Wulfmeier",
"Andrea Vedaldi",
"Ingmar Posner"
] | [] | 2018-06-14T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/financial-risk-and-returns-prediction-with | 1806.05876 | null | null | Financial Risk and Returns Prediction with Modular Networked Learning | An artificial agent for financial risk and returns' prediction is built with
a modular cognitive system comprised of interconnected recurrent neural
networks, such that the agent learns to predict the financial returns, and
learns to predict the squared deviation around these predicted returns. These
two expectations a... | null | http://arxiv.org/abs/1806.05876v1 | http://arxiv.org/pdf/1806.05876v1.pdf | null | [
"Carlos Pedro Gonçalves"
] | [
"Prediction"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/data-efficient-design-exploration-through | 1806.05865 | null | null | Data-Efficient Design Exploration through Surrogate-Assisted Illumination | Design optimization techniques are often used at the beginning of the design
process to explore the space of possible designs. In these domains illumination
algorithms, such as MAP-Elites, are promising alternatives to classic
optimization algorithms because they produce diverse, high-quality solutions in
a single run,... | Design optimization techniques are often used at the beginning of the design process to explore the space of possible designs. | http://arxiv.org/abs/1806.05865v1 | http://arxiv.org/pdf/1806.05865v1.pdf | null | [
"Adam Gaier",
"Alexander Asteroth",
"Jean-Baptiste Mouret"
] | [] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/deeplaser-practical-fault-attack-on-deep | 1806.05859 | null | null | DeepLaser: Practical Fault Attack on Deep Neural Networks | As deep learning systems are widely adopted in safety- and security-critical
applications, such as autonomous vehicles, banking systems, etc., malicious
faults and attacks become a tremendous concern, which potentially could lead to
catastrophic consequences. In this paper, we initiate the first study of
leveraging phy... | null | http://arxiv.org/abs/1806.05859v2 | http://arxiv.org/pdf/1806.05859v2.pdf | null | [
"Jakub Breier",
"Xiaolu Hou",
"Dirmanto Jap",
"Lei Ma",
"Shivam Bhasin",
"Yang Liu"
] | [
"Autonomous Vehicles"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/semantic-variation-in-online-communities-of | 1806.05847 | null | null | Semantic Variation in Online Communities of Practice | We introduce a framework for quantifying semantic variation of common words
in Communities of Practice and in sets of topic-related communities. We show
that while some meaning shifts are shared across related communities, others
are community-specific, and therefore independent from the discussed topic. We
propose suc... | null | http://arxiv.org/abs/1806.05847v1 | http://arxiv.org/pdf/1806.05847v1.pdf | WS 2017 1 | [
"Marco Del Tredici",
"Raquel Fernández"
] | [
"Language Modelling"
] | 2018-06-15T00:00:00 | https://aclanthology.org/W17-6804 | https://aclanthology.org/W17-6804.pdf | semantic-variation-in-online-communities-of-1 | null | [] |
https://paperswithcode.com/paper/a-covariance-matrix-self-adaptation-evolution | 1806.05845 | null | null | A Covariance Matrix Self-Adaptation Evolution Strategy for Optimization under Linear Constraints | This paper addresses the development of a covariance matrix self-adaptation
evolution strategy (CMSA-ES) for solving optimization problems with linear
constraints. The proposed algorithm is referred to as Linear Constraint CMSA-ES
(lcCMSA-ES). It uses a specially built mutation operator together with repair
by projecti... | null | http://arxiv.org/abs/1806.05845v2 | http://arxiv.org/pdf/1806.05845v2.pdf | null | [
"Patrick Spettel",
"Hans-Georg Beyer",
"Michael Hellwig"
] | [] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/co-clustering-via-information-theoretic | 1801.00584 | null | null | Co-Clustering via Information-Theoretic Markov Aggregation | We present an information-theoretic cost function for co-clustering, i.e.,
for simultaneous clustering of two sets based on similarities between their
elements. By constructing a simple random walk on the corresponding bipartite
graph, our cost function is derived from a recently proposed generalized
framework for info... | null | http://arxiv.org/abs/1801.00584v2 | http://arxiv.org/pdf/1801.00584v2.pdf | null | [
"Clemens Bloechl",
"Rana Ali Amjad",
"Bernhard C. Geiger"
] | [
"Clustering"
] | 2018-01-02T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/real-time-monocular-visual-odometry-for | 1806.05842 | null | null | Real-time Monocular Visual Odometry for Turbid and Dynamic Underwater Environments | In the context of robotic underwater operations, the visual degradations induced by the medium properties make difficult the exclusive use of cameras for localization purpose. Hence, most localization methods are based on expensive navigational sensors associated with acoustic positioning. On the other hand, visual odo... | null | https://arxiv.org/abs/1806.05842v3 | https://arxiv.org/pdf/1806.05842v3.pdf | null | [
"Maxime Ferrera",
"Julien Moras",
"Pauline Trouvé-Peloux",
"Vincent Creuze"
] | [
"Monocular Visual Odometry",
"Optical Flow Estimation",
"Visual Odometry"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/the-road-to-success-assessing-the-fate-of | 1806.05838 | null | null | The Road to Success: Assessing the Fate of Linguistic Innovations in Online Communities | We investigate the birth and diffusion of lexical innovations in a large
dataset of online social communities. We build on sociolinguistic theories and
focus on the relation between the spread of a novel term and the social role of
the individuals who use it, uncovering characteristics of innovators and
adopters. Final... | We investigate the birth and diffusion of lexical innovations in a large dataset of online social communities. | http://arxiv.org/abs/1806.05838v1 | http://arxiv.org/pdf/1806.05838v1.pdf | COLING 2018 8 | [
"Marco Del Tredici",
"Raquel Fernández"
] | [
"Relation"
] | 2018-06-15T00:00:00 | https://aclanthology.org/C18-1135 | https://aclanthology.org/C18-1135.pdf | the-road-to-success-assessing-the-fate-of-1 | null | [] |
https://paperswithcode.com/paper/on-the-exact-minimization-of-saturated-loss | 1806.05833 | null | null | On the exact minimization of saturated loss functions for robust regression and subspace estimation | This paper deals with robust regression and subspace estimation and more
precisely with the problem of minimizing a saturated loss function. In
particular, we focus on computational complexity issues and show that an exact
algorithm with polynomial time-complexity with respect to the number of data
can be devised for r... | null | http://arxiv.org/abs/1806.05833v2 | http://arxiv.org/pdf/1806.05833v2.pdf | null | [
"Fabien Lauer"
] | [
"General Classification",
"regression"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/selfless-sequential-learning | 1806.05421 | null | Bkxbrn0cYX | Selfless Sequential Learning | Sequential learning, also called lifelong learning, studies the problem of
learning tasks in a sequence with access restricted to only the data of the
current task. In this paper we look at a scenario with fixed model capacity,
and postulate that the learning process should not be selfish, i.e. it should
account for fu... | In particular, we propose a novel regularizer, that encourages representation sparsity by means of neural inhibition. | http://arxiv.org/abs/1806.05421v5 | http://arxiv.org/pdf/1806.05421v5.pdf | ICLR 2019 5 | [
"Rahaf Aljundi",
"Marcus Rohrbach",
"Tinne Tuytelaars"
] | [
"Lifelong learning"
] | 2018-06-14T00:00:00 | https://openreview.net/forum?id=Bkxbrn0cYX | https://openreview.net/pdf?id=Bkxbrn0cYX | selfless-sequential-learning-1 | null | [] |
https://paperswithcode.com/paper/temporal-stability-in-predictive-process | 1712.04165 | null | null | Temporal Stability in Predictive Process Monitoring | Predictive process monitoring is concerned with the analysis of events
produced during the execution of a business process in order to predict as
early as possible the final outcome of an ongoing case. Traditionally,
predictive process monitoring methods are optimized with respect to accuracy.
However, in environments ... | We then show that temporal stability can be enhanced by hyperparameter-optimizing random forests and XGBoost classifiers with respect to inter-run stability. | http://arxiv.org/abs/1712.04165v3 | http://arxiv.org/pdf/1712.04165v3.pdf | null | [
"Irene Teinemaa",
"Marlon Dumas",
"Anna Leontjeva",
"Fabrizio Maria Maggi"
] | [
"Binary Classification",
"Predictive Process Monitoring",
"Time Series",
"Time Series Analysis"
] | 2017-12-12T00:00:00 | null | null | null | null | [
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\... |
https://paperswithcode.com/paper/three-dimensional-deep-learning-approach-for | 1806.05824 | null | null | Three dimensional Deep Learning approach for remote sensing image classification | Recently, a variety of approaches has been enriching the field of Remote
Sensing (RS) image processing and analysis. Unfortunately, existing methods
remain limited faced to the rich spatio-spectral content of today's large
datasets. It would seem intriguing to resort to Deep Learning (DL) based
approaches at this stage... | null | http://arxiv.org/abs/1806.05824v1 | http://arxiv.org/pdf/1806.05824v1.pdf | null | [
"Amina Ben Hamida",
"A. Benoit",
"Patrick Lambert",
"Chokri Ben Amar"
] | [
"Classification",
"Deep Learning",
"General Classification",
"image-classification",
"Image Classification",
"Remote Sensing Image Classification",
"Specificity"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/primal-dual-residual-networks | 1806.05823 | null | null | Primal-dual residual networks | In this work, we propose a deep neural network architecture motivated by
primal-dual splitting methods from convex optimization. We show theoretically
that there exists a close relation between the derived architecture and
residual networks, and further investigate this connection in numerical
experiments. Moreover, we... | null | http://arxiv.org/abs/1806.05823v1 | http://arxiv.org/pdf/1806.05823v1.pdf | null | [
"Christoph Brauer",
"Dirk Lorenz"
] | [] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/bubblerank-safe-online-learning-to-rerank | 1806.05819 | null | null | BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback | In this paper, we study the problem of safe online learning to re-rank, where user feedback is used to improve the quality of displayed lists. Learning to rank has traditionally been studied in two settings. In the offline setting, rankers are typically learned from relevance labels created by judges. This approach has... | null | https://arxiv.org/abs/1806.05819v2 | https://arxiv.org/pdf/1806.05819v2.pdf | null | [
"Chang Li",
"Branislav Kveton",
"Tor Lattimore",
"Ilya Markov",
"Maarten de Rijke",
"Csaba Szepesvari",
"Masrour Zoghi"
] | [
"Learning-To-Rank",
"Re-Ranking",
"Safe Exploration"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/safe-active-feature-selection-for-sparse | 1806.05817 | null | null | Safe Active Feature Selection for Sparse Learning | We present safe active incremental feature selection~(SAIF) to scale up the
computation of LASSO solutions. SAIF does not require a solution from a heavier
penalty parameter as in sequential screening or updating the full model for
each iteration as in dynamic screening. Different from these existing screening
methods,... | null | http://arxiv.org/abs/1806.05817v2 | http://arxiv.org/pdf/1806.05817v2.pdf | null | [
"Shaogang Ren",
"Jianhua Z. Huang",
"Shuai Huang",
"Xiaoning Qian"
] | [
"feature selection",
"Sparse Learning"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/sgm-sequence-generation-model-for-multi-label | 1806.04822 | null | null | SGM: Sequence Generation Model for Multi-label Classification | Multi-label classification is an important yet challenging task in natural
language processing. It is more complex than single-label classification in
that the labels tend to be correlated. Existing methods tend to ignore the
correlations between labels. Besides, different parts of the text can
contribute differently f... | Further analysis of experimental results demonstrates that the proposed methods not only capture the correlations between labels, but also select the most informative words automatically when predicting different labels. | http://arxiv.org/abs/1806.04822v3 | http://arxiv.org/pdf/1806.04822v3.pdf | COLING 2018 8 | [
"Pengcheng Yang",
"Xu sun",
"Wei Li",
"Shuming Ma",
"Wei Wu",
"Houfeng Wang"
] | [
"Classification",
"Decoder",
"General Classification",
"model",
"Multi-Label Classification",
"MUlTI-LABEL-ClASSIFICATION"
] | 2018-06-13T00:00:00 | https://aclanthology.org/C18-1330 | https://aclanthology.org/C18-1330.pdf | sgm-sequence-generation-model-for-multi-label-1 | null | [] |
https://paperswithcode.com/paper/best-sources-forward-domain-generalization | 1806.05810 | null | null | Best sources forward: domain generalization through source-specific nets | A long standing problem in visual object categorization is the ability of
algorithms to generalize across different testing conditions. The problem has
been formalized as a covariate shift among the probability distributions
generating the training data (source) and the test data (target) and several
domain adaptation ... | null | http://arxiv.org/abs/1806.05810v1 | http://arxiv.org/pdf/1806.05810v1.pdf | null | [
"Massimiliano Mancini",
"Samuel Rota Bulò",
"Barbara Caputo",
"Elisa Ricci"
] | [
"Domain Adaptation",
"Domain Generalization",
"Object Categorization"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/molecular-generative-model-based-on | 1806.05805 | null | null | Molecular generative model based on conditional variational autoencoder for de novo molecular design | We propose a molecular generative model based on the conditional variational
autoencoder for de novo molecular design. It is specialized to control multiple
molecular properties simultaneously by imposing them on a latent space. As a
proof of concept, we demonstrate that it can be used to generate drug-like
molecules w... | We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design. | http://arxiv.org/abs/1806.05805v1 | http://arxiv.org/pdf/1806.05805v1.pdf | null | [
"Jaechang Lim",
"Seongok Ryu",
"Jin Woo Kim",
"Woo Youn Kim"
] | [] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/hybrid-approach-of-relation-network-and | 1711.05859 | null | null | Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification | Network biology has been successfully used to help reveal complex mechanisms
of disease, especially cancer. On the other hand, network biology requires
in-depth knowledge to construct disease-specific networks, but our current
knowledge is very limited even with the recent advances in human cancer
biology. Deep learnin... | In this paper, we proposed a hybrid model, which integrates two key components 1) graph convolution neural network (graph CNN) and 2) relation network (RN). | http://arxiv.org/abs/1711.05859v3 | http://arxiv.org/pdf/1711.05859v3.pdf | null | [
"Sungmin Rhee",
"Seokjun Seo",
"Sun Kim"
] | [
"Classification",
"Deep Learning",
"General Classification",
"Relation",
"Relation Network",
"Survival Analysis"
] | 2017-11-16T00:00:00 | null | null | null | null | [
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a... |
https://paperswithcode.com/paper/learning-to-act-properly-predicting-and | 1712.07576 | null | null | Learning to Act Properly: Predicting and Explaining Affordances from Images | We address the problem of affordance reasoning in diverse scenes that appear
in the real world. Affordances relate the agent's actions to their effects when
taken on the surrounding objects. In our work, we take the egocentric view of
the scene, and aim to reason about action-object affordances that respect both
the ph... | null | http://arxiv.org/abs/1712.07576v2 | http://arxiv.org/pdf/1712.07576v2.pdf | CVPR 2018 6 | [
"Ching-Yao Chuang",
"Jiaman Li",
"Antonio Torralba",
"Sanja Fidler"
] | [
"Visual Reasoning"
] | 2017-12-20T00:00:00 | http://openaccess.thecvf.com/content_cvpr_2018/html/Chuang_Learning_to_Act_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Chuang_Learning_to_Act_CVPR_2018_paper.pdf | learning-to-act-properly-predicting-and-1 | null | [] |
https://paperswithcode.com/paper/weakly-supervised-deep-image-hashing-through | 1806.05804 | null | null | Weakly Supervised Deep Image Hashing through Tag Embeddings | Many approaches to semantic image hashing have been formulated as supervised
learning problems that utilize images and label information to learn the binary
hash codes. However, large-scale labeled image data is expensive to obtain,
thus imposing a restriction on the usage of such algorithms. On the other hand,
unlabel... | We utilize the information contained in the user-generated tags associated with the images to learn the hash codes. | http://arxiv.org/abs/1806.05804v3 | http://arxiv.org/pdf/1806.05804v3.pdf | CVPR 2019 6 | [
"Vijetha Gattupalli",
"Yaoxin Zhuo",
"Baoxin Li"
] | [
"Deep Hashing",
"Image Retrieval",
"Retrieval",
"TAG",
"Weakly-supervised Learning"
] | 2018-06-15T00:00:00 | http://openaccess.thecvf.com/content_CVPR_2019/html/Gattupalli_Weakly_Supervised_Deep_Image_Hashing_Through_Tag_Embeddings_CVPR_2019_paper.html | http://openaccess.thecvf.com/content_CVPR_2019/papers/Gattupalli_Weakly_Supervised_Deep_Image_Hashing_Through_Tag_Embeddings_CVPR_2019_paper.pdf | weakly-supervised-deep-image-hashing-through-1 | null | [] |
https://paperswithcode.com/paper/wikiref-wikilinks-as-a-route-to-recommending | 1806.04092 | null | null | WikiRef: Wikilinks as a route to recommending appropriate references for scientific Wikipedia pages | The exponential increase in the usage of Wikipedia as a key source of
scientific knowledge among the researchers is making it absolutely necessary to
metamorphose this knowledge repository into an integral and self-contained
source of information for direct utilization. Unfortunately, the references
which support the c... | null | http://arxiv.org/abs/1806.04092v2 | http://arxiv.org/pdf/1806.04092v2.pdf | COLING 2018 8 | [
"Abhik Jana",
"Pranjal Kanojiya",
"Pawan Goyal",
"Animesh Mukherjee"
] | [] | 2018-06-11T00:00:00 | https://aclanthology.org/C18-1032 | https://aclanthology.org/C18-1032.pdf | wikiref-wikilinks-as-a-route-to-recommending-2 | null | [] |
https://paperswithcode.com/paper/disentangled-person-image-generation | 1712.02621 | null | null | Disentangled Person Image Generation | Generating novel, yet realistic, images of persons is a challenging task due
to the complex interplay between the different image factors, such as the
foreground, background and pose information. In this work, we aim at generating
such images based on a novel, two-stage reconstruction pipeline that learns a
disentangle... | Generating novel, yet realistic, images of persons is a challenging task due to the complex interplay between the different image factors, such as the foreground, background and pose information. | http://arxiv.org/abs/1712.02621v4 | http://arxiv.org/pdf/1712.02621v4.pdf | CVPR 2018 6 | [
"Liqian Ma",
"Qianru Sun",
"Stamatios Georgoulis",
"Luc van Gool",
"Bernt Schiele",
"Mario Fritz"
] | [
"Gesture-to-Gesture Translation",
"Image Generation",
"Person Re-Identification",
"Pose Transfer"
] | 2017-12-07T00:00:00 | http://openaccess.thecvf.com/content_cvpr_2018/html/Ma_Disentangled_Person_Image_CVPR_2018_paper.html | http://openaccess.thecvf.com/content_cvpr_2018/papers/Ma_Disentangled_Person_Image_CVPR_2018_paper.pdf | disentangled-person-image-generation-1 | null | [] |
https://paperswithcode.com/paper/scalable-factorized-hierarchical-variational | 1804.03201 | null | null | Scalable Factorized Hierarchical Variational Autoencoder Training | Deep generative models have achieved great success in unsupervised learning
with the ability to capture complex nonlinear relationships between latent
generating factors and observations. Among them, a factorized hierarchical
variational autoencoder (FHVAE) is a variational inference-based model that
formulates a hiera... | Deep generative models have achieved great success in unsupervised learning with the ability to capture complex nonlinear relationships between latent generating factors and observations. | http://arxiv.org/abs/1804.03201v2 | http://arxiv.org/pdf/1804.03201v2.pdf | null | [
"Wei-Ning Hsu",
"James Glass"
] | [
"Disentanglement",
"Hyperparameter Optimization",
"Robust Speech Recognition",
"Speaker Verification",
"speech-recognition",
"Speech Recognition",
"Variational Inference",
"Voice Conversion"
] | 2018-04-09T00:00:00 | null | null | null | null | [
{
"code_snippet_url": null,
"description": "Please enter a description about the method here",
"full_name": "Interpretability",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Image Models** are methods that build representations of images f... |
https://paperswithcode.com/paper/learning-6-dof-grasping-interaction-via-deep | 1708.07303 | null | null | Learning 6-DOF Grasping Interaction via Deep Geometry-aware 3D Representations | This paper focuses on the problem of learning 6-DOF grasping with a parallel
jaw gripper in simulation. We propose the notion of a geometry-aware
representation in grasping based on the assumption that knowledge of 3D
geometry is at the heart of interaction. Our key idea is constraining and
regularizing grasping intera... | Our contributions are fourfold: (1) To best of our knowledge, we are presenting for the first time a method to learn a 6-DOF grasping net from RGBD input; (2) We build a grasping dataset from demonstrations in virtual reality with rich sensory and interaction annotations. | http://arxiv.org/abs/1708.07303v4 | http://arxiv.org/pdf/1708.07303v4.pdf | null | [
"Xinchen Yan",
"Jasmine Hsu",
"Mohi Khansari",
"Yunfei Bai",
"Arkanath Pathak",
"Abhinav Gupta",
"James Davidson",
"Honglak Lee"
] | [
"3D geometry",
"3D Geometry Prediction",
"3D Shape Modeling",
"Data Augmentation"
] | 2017-08-24T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/satr-dl-improving-surgical-skill-assessment | 1806.05798 | null | null | SATR-DL: Improving Surgical Skill Assessment and Task Recognition in Robot-assisted Surgery with Deep Neural Networks | Purpose: This paper focuses on an automated analysis of surgical motion
profiles for objective skill assessment and task recognition in robot-assisted
surgery. Existing techniques heavily rely on conventional statistic measures or
shallow modelings based on hand-engineered features and gesture segmentation.
Such develo... | null | http://arxiv.org/abs/1806.05798v1 | http://arxiv.org/pdf/1806.05798v1.pdf | null | [
"Ziheng Wang",
"Ann Majewicz Fey"
] | [
"Representation Learning"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/supervised-speech-separation-based-on-deep | 1708.07524 | null | null | Supervised Speech Separation Based on Deep Learning: An Overview | Speech separation is the task of separating target speech from background
interference. Traditionally, speech separation is studied as a signal
processing problem. A more recent approach formulates speech separation as a
supervised learning problem, where the discriminative patterns of speech,
speakers, and background ... | null | http://arxiv.org/abs/1708.07524v2 | http://arxiv.org/pdf/1708.07524v2.pdf | null | [
"DeLiang Wang",
"Jitong Chen"
] | [
"Deep Learning",
"Speaker Separation",
"Speech Dereverberation",
"Speech Enhancement",
"Speech Separation"
] | 2017-08-24T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/deep-learning-with-convolutional-neural-2 | 1806.05796 | null | null | Deep Learning with Convolutional Neural Network for Objective Skill Evaluation in Robot-assisted Surgery | With the advent of robot-assisted surgery, the role of data-driven approaches
to integrate statistics and machine learning is growing rapidly with prominent
interests in objective surgical skill assessment. However, most existing work
requires translating robot motion kinematics into intermediate features or
gesture se... | null | http://arxiv.org/abs/1806.05796v2 | http://arxiv.org/pdf/1806.05796v2.pdf | null | [
"Ziheng Wang",
"Ann Majewicz Fey"
] | [
"Time Series",
"Time Series Analysis"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/rapidnn-in-memory-deep-neural-network | 1806.05794 | null | null | RAPIDNN: In-Memory Deep Neural Network Acceleration Framework | Deep neural networks (DNN) have demonstrated effectiveness for various
applications such as image processing, video segmentation, and speech
recognition. Running state-of-the-art DNNs on current systems mostly relies on
either generalpurpose processors, ASIC designs, or FPGA accelerators, all of
which suffer from data ... | null | http://arxiv.org/abs/1806.05794v4 | http://arxiv.org/pdf/1806.05794v4.pdf | null | [
"Mohsen Imani",
"Mohammad Samragh",
"Yeseong Kim",
"Saransh Gupta",
"Farinaz Koushanfar",
"Tajana Rosing"
] | [
"Clustering",
"speech-recognition",
"Speech Recognition",
"Video Segmentation",
"Video Semantic Segmentation"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/recurrent-multiresolution-convolutional | 1806.05793 | null | null | Recurrent Multiresolution Convolutional Networks for VHR Image Classification | Classification of very high resolution (VHR) satellite images has three major
challenges: 1) inherent low intra-class and high inter-class spectral
similarities, 2) mismatching resolution of available bands, and 3) the need to
regularize noisy classification maps. Conventional methods have addressed these
challenges by... | null | http://arxiv.org/abs/1806.05793v1 | http://arxiv.org/pdf/1806.05793v1.pdf | null | [
"John Ray Bergado",
"Claudio Persello",
"Alfred Stein"
] | [
"Classification",
"General Classification",
"image-classification",
"Image Classification",
"Land Cover Classification",
"Pansharpening"
] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/mobilefacenets-efficient-cnns-for-accurate | 1804.07573 | null | null | MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices | Face Analysis Project on MXNet | Face Analysis Project on MXNet | http://arxiv.org/abs/1804.07573v4 | http://arxiv.org/pdf/1804.07573v4.pdf | null | [
"Sheng Chen",
"Yang Liu",
"Xiang Gao",
"Zhen Han"
] | [
"Lightweight Face Recognition"
] | 2018-04-20T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/monaural-source-enhancement-maximizing-source | 1806.05791 | null | null | Monaural source enhancement maximizing source-to-distortion ratio via automatic differentiation | Recently, deep neural network (DNN) has made a breakthrough in monaural
source enhancement. Through a training step by using a large amount of data,
DNN estimates a mapping between mixed signals and clean signals. At this time,
we use an objective function that numerically expresses the quality of a
mapping by DNN. In ... | null | http://arxiv.org/abs/1806.05791v1 | http://arxiv.org/pdf/1806.05791v1.pdf | null | [
"Hiroaki Nakajima",
"Yu Takahashi",
"Kazunobu Kondo",
"Yuji Hisaminato"
] | [] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/image-classification-and-retrieval-with | 1806.05789 | null | null | Image classification and retrieval with random depthwise signed convolutional neural networks | We propose a random convolutional neural network to generate a feature space
in which we study image classification and retrieval performance. Put briefly
we apply random convolutional blocks followed by global average pooling to
generate a new feature, and we repeat this k times to produce a k-dimensional
feature spac... | We find that k-nearest neighbor gives a comparable precision on the Corel Princeton Image Similarity Benchmark than if we were to use the final layer of trained networks. | http://arxiv.org/abs/1806.05789v3 | http://arxiv.org/pdf/1806.05789v3.pdf | null | [
"Yunzhe Xue",
"Usman Roshan"
] | [
"General Classification",
"image-classification",
"Image Classification",
"Retrieval"
] | 2018-06-15T00:00:00 | null | null | null | null | [
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/baa592b215804927e28638f6a7f3318cbc411d49/torchvision/models/resnet.py#L157",
"description": "**Global Average Pooling** is a pooling operation designed to replace fully connected layers in classical CNNs. The idea is to generate one feature map f... |
https://paperswithcode.com/paper/a-survey-of-automatic-facial-micro-expression | 1806.05781 | null | null | A Survey of Automatic Facial Micro-expression Analysis: Databases, Methods and Challenges | Over the last few years, automatic facial micro-expression analysis has
garnered increasing attention from experts across different disciplines because
of its potential applications in various fields such as clinical diagnosis,
forensic investigation and security systems. Advances in computer algorithms
and video acqui... | null | http://arxiv.org/abs/1806.05781v1 | http://arxiv.org/pdf/1806.05781v1.pdf | null | [
"Yee-Hui Oh",
"John See",
"Anh Cat Le Ngo",
"Raphael Chung-Wei Phan",
"Vishnu Monn Baskaran"
] | [] | 2018-06-15T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/surprising-negative-results-for-generative | 1806.05780 | null | BJl4f2A5tQ | Surprising Negative Results for Generative Adversarial Tree Search | While many recent advances in deep reinforcement learning (RL) rely on model-free methods, model-based approaches remain an alluring prospect for their potential to exploit unsupervised data to learn environment model. In this work, we provide an extensive study on the design of deep generative models for RL environmen... | We deploy this model and propose generative adversarial tree search (GATS) a deep RL algorithm that learns the environment model and implements Monte Carlo tree search (MCTS) on the learned model for planning. | https://arxiv.org/abs/1806.05780v4 | https://arxiv.org/pdf/1806.05780v4.pdf | ICLR 2019 5 | [
"Kamyar Azizzadenesheli",
"Brandon Yang",
"Weitang Liu",
"Zachary C. Lipton",
"Animashree Anandkumar"
] | [
"Atari Games",
"Deep Reinforcement Learning",
"Reinforcement Learning",
"Reinforcement Learning (RL)"
] | 2018-06-15T00:00:00 | https://openreview.net/forum?id=BJl4f2A5tQ | https://openreview.net/pdf?id=BJl4f2A5tQ | surprising-negative-results-for-generative-1 | null | [
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a... |
https://paperswithcode.com/paper/deep-learning-approximation-zero-shot-neural | 1806.05779 | null | SyGdZXSboX | Deep Learning Approximation: Zero-Shot Neural Network Speedup | Neural networks offer high-accuracy solutions to a range of problems, but are
costly to run in production systems because of computational and memory
requirements during a forward pass. Given a trained network, we propose a
techique called Deep Learning Approximation to build a faster network in a tiny
fraction of the ... | null | http://arxiv.org/abs/1806.05779v1 | http://arxiv.org/pdf/1806.05779v1.pdf | null | [
"Michele Pratusevich"
] | [
"Deep Learning"
] | 2018-06-15T00:00:00 | https://openreview.net/forum?id=SyGdZXSboX | https://openreview.net/pdf?id=SyGdZXSboX | null | null | [] |
https://paperswithcode.com/paper/gradient-descent-learns-one-hidden-layer-cnn | 1712.00779 | null | null | Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima | We consider the problem of learning a one-hidden-layer neural network with
non-overlapping convolutional layer and ReLU activation, i.e., $f(\mathbf{Z},
\mathbf{w}, \mathbf{a}) = \sum_j a_j\sigma(\mathbf{w}^T\mathbf{Z}_j)$, in which
both the convolutional weights $\mathbf{w}$ and the output weights $\mathbf{a}$
are par... | null | http://arxiv.org/abs/1712.00779v2 | http://arxiv.org/pdf/1712.00779v2.pdf | ICML 2018 7 | [
"Simon S. Du",
"Jason D. Lee",
"Yuandong Tian",
"Barnabas Poczos",
"Aarti Singh"
] | [] | 2017-12-03T00:00:00 | https://icml.cc/Conferences/2018/Schedule?showEvent=1922 | http://proceedings.mlr.press/v80/du18b/du18b.pdf | gradient-descent-learns-one-hidden-layer-cnn-1 | null | [
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-... |
https://paperswithcode.com/paper/on-the-power-of-over-parametrization-in-1 | 1803.01206 | null | null | On the Power of Over-parametrization in Neural Networks with Quadratic Activation | We provide new theoretical insights on why over-parametrization is effective
in learning neural networks. For a $k$ hidden node shallow network with
quadratic activation and $n$ training data points, we show as long as $ k \ge
\sqrt{2n}$, over-parametrization enables local search algorithms to find a
\emph{globally} op... | We provide new theoretical insights on why over-parametrization is effective in learning neural networks. | http://arxiv.org/abs/1803.01206v2 | http://arxiv.org/pdf/1803.01206v2.pdf | ICML 2018 | [
"Simon S. Du",
"Jason D. Lee"
] | [] | 2018-03-03T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/hardware-trojan-attacks-on-neural-networks | 1806.05768 | null | null | Hardware Trojan Attacks on Neural Networks | With the rising popularity of machine learning and the ever increasing demand
for computational power, there is a growing need for hardware optimized
implementations of neural networks and other machine learning models. As the
technology evolves, it is also plausible that machine learning or artificial
intelligence wil... | null | http://arxiv.org/abs/1806.05768v1 | http://arxiv.org/pdf/1806.05768v1.pdf | null | [
"Joseph Clements",
"Yingjie Lao"
] | [
"BIG-bench Machine Learning",
"Neural Network Security"
] | 2018-06-14T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/motion-planning-networks | 1806.05767 | null | null | Motion Planning Networks | Fast and efficient motion planning algorithms are crucial for many
state-of-the-art robotics applications such as self-driving cars. Existing
motion planning methods become ineffective as their computational complexity
increases exponentially with the dimensionality of the motion planning problem.
To address this issue... | Fast and efficient motion planning algorithms are crucial for many state-of-the-art robotics applications such as self-driving cars. | http://arxiv.org/abs/1806.05767v2 | http://arxiv.org/pdf/1806.05767v2.pdf | null | [
"Ahmed H. Qureshi",
"Anthony Simeonov",
"Mayur J. Bency",
"Michael C. Yip"
] | [
"Motion Planning",
"Self-Driving Cars",
"Transfer Learning"
] | 2018-06-14T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/generative-adversarial-networks-and | 1806.05764 | null | null | Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution | Video super-resolution (VSR) has become one of the most critical problems in
video processing. In the deep learning literature, recent works have shown the
benefits of using adversarial-based and perceptual losses to improve the
performance on various image restoration tasks; however, these have yet to be
applied for v... | null | http://arxiv.org/abs/1806.05764v2 | http://arxiv.org/pdf/1806.05764v2.pdf | null | [
"Alice Lucas",
"Santiago Lopez Tapia",
"Rafael Molina",
"Aggelos K. Katsaggelos"
] | [
"Generative Adversarial Network",
"Image Restoration",
"SSIM",
"Super-Resolution",
"Video Super-Resolution"
] | 2018-06-14T00:00:00 | null | null | null | null | [
{
"code_snippet_url": "",
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively, a... |
https://paperswithcode.com/paper/pac-bayes-control-synthesizing-controllers | 1806.04225 | null | null | PAC-Bayes Control: Learning Policies that Provably Generalize to Novel Environments | Our goal is to learn control policies for robots that provably generalize well to novel environments given a dataset of example environments. The key technical idea behind our approach is to leverage tools from generalization theory in machine learning by exploiting a precise analogy (which we present in the form of a ... | The key technical idea behind our approach is to leverage tools from generalization theory in machine learning by exploiting a precise analogy (which we present in the form of a reduction) between generalization of control policies to novel environments and generalization of hypotheses in the supervised learning settin... | https://arxiv.org/abs/1806.04225v5 | https://arxiv.org/pdf/1806.04225v5.pdf | null | [
"Anirudha Majumdar",
"Alec Farid",
"Anoopkumar Sonar"
] | [] | 2018-06-11T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/insights-on-representational-similarity-in | 1806.05759 | null | null | Insights on representational similarity in neural networks with canonical correlation | Comparing different neural network representations and determining how
representations evolve over time remain challenging open questions in our
understanding of the function of neural networks. Comparing representations in
neural networks is fundamentally difficult as the structure of representations
varies greatly, e... | Comparing representations in neural networks is fundamentally difficult as the structure of representations varies greatly, even across groups of networks trained on identical tasks, and over the course of training. | http://arxiv.org/abs/1806.05759v3 | http://arxiv.org/pdf/1806.05759v3.pdf | NeurIPS 2018 12 | [
"Ari S. Morcos",
"Maithra Raghu",
"Samy Bengio"
] | [] | 2018-06-14T00:00:00 | http://papers.nips.cc/paper/7815-insights-on-representational-similarity-in-neural-networks-with-canonical-correlation | http://papers.nips.cc/paper/7815-insights-on-representational-similarity-in-neural-networks-with-canonical-correlation.pdf | insights-on-representational-similarity-in-1 | null | [] |
https://paperswithcode.com/paper/enhanced-local-binary-patterns-for-automatic | 1702.03349 | null | null | Enhanced Local Binary Patterns for Automatic Face Recognition | This paper presents a novel automatic face recognition approach based on
local binary patterns. This descriptor considers a local neighbourhood of a
pixel to compute the feature vector values. This method is not very robust to
handle image noise, variances and different illumination conditions. We address
these issues ... | null | http://arxiv.org/abs/1702.03349v2 | http://arxiv.org/pdf/1702.03349v2.pdf | null | [
"Pavel Král",
"Ladislav Lenc",
"Antonín Vrba"
] | [
"Face Recognition"
] | 2017-02-10T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/sliced-wasserstein-autoencoder-an | 1804.01947 | null | null | Sliced-Wasserstein Autoencoder: An Embarrassingly Simple Generative Model | In this paper we study generative modeling via autoencoders while using the
elegant geometric properties of the optimal transport (OT) problem and the
Wasserstein distances. We introduce Sliced-Wasserstein Autoencoders (SWAE),
which are generative models that enable one to shape the distribution of the
latent space int... | In short, we regularize the autoencoder loss with the sliced-Wasserstein distance between the distribution of the encoded training samples and a predefined samplable distribution. | http://arxiv.org/abs/1804.01947v3 | http://arxiv.org/pdf/1804.01947v3.pdf | null | [
"Soheil Kolouri",
"Phillip E. Pope",
"Charles E. Martin",
"Gustavo K. Rohde"
] | [
"model"
] | 2018-04-05T00:00:00 | null | null | null | null | [
{
"code_snippet_url": "",
"description": "In today’s digital age, Solana has become more than just a buzzword—it’s a revolutionary way to manage and invest your money. But just like with any advanced technology, users sometimes face issues that can be frustrating or even alarming. Whether you're dealing wit... |
https://paperswithcode.com/paper/detecting-speech-act-types-in-developer | 1806.05130 | null | null | Detecting Speech Act Types in Developer Question/Answer Conversations During Bug Repair | This paper targets the problem of speech act detection in conversations about
bug repair. We conduct a "Wizard of Oz" experiment with 30 professional
programmers, in which the programmers fix bugs for two hours, and use a
simulated virtual assistant for help. Then, we use an open coding manual
annotation procedure to i... | null | http://arxiv.org/abs/1806.05130v3 | http://arxiv.org/pdf/1806.05130v3.pdf | null | [
"Andrew Wood",
"Paige Rodeghero",
"Ameer Armaly",
"Collin McMillan"
] | [] | 2018-06-13T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/probabilistic-tools-for-the-analysis-of | 1801.06733 | null | null | Probabilistic Tools for the Analysis of Randomized Optimization Heuristics | This chapter collects several probabilistic tools that proved to be useful in the analysis of randomized search heuristics. This includes classic material like Markov, Chebyshev and Chernoff inequalities, but also lesser known topics like stochastic domination and coupling or Chernoff bounds for geometrically distribut... | null | https://arxiv.org/abs/1801.06733v6 | https://arxiv.org/pdf/1801.06733v6.pdf | null | [
"Benjamin Doerr"
] | [] | 2018-01-20T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/using-clinical-narratives-and-structured-data | 1806.04818 | null | null | Using Clinical Narratives and Structured Data to Identify Distant Recurrences in Breast Cancer | Accurately identifying distant recurrences in breast cancer from the
Electronic Health Records (EHR) is important for both clinical care and
secondary analysis. Although multiple applications have been developed for
computational phenotyping in breast cancer, distant recurrence identification
still relies heavily on ma... | null | http://arxiv.org/abs/1806.04818v2 | http://arxiv.org/pdf/1806.04818v2.pdf | null | [
"Zexian Zeng",
"Ankita Roy",
"Xiaoyu Li",
"Sasa Espino",
"Susan Clare",
"Seema Khan",
"Yuan Luo"
] | [
"Computational Phenotyping"
] | 2018-06-13T00:00:00 | null | null | null | null | [] |
https://paperswithcode.com/paper/natural-language-processing-for-ehr-based | 1806.04820 | null | null | Natural Language Processing for EHR-Based Computational Phenotyping | This article reviews recent advances in applying natural language processing
(NLP) to Electronic Health Records (EHRs) for computational phenotyping.
NLP-based computational phenotyping has numerous applications including
diagnosis categorization, novel phenotype discovery, clinical trial screening,
pharmacogenomics, d... | null | http://arxiv.org/abs/1806.04820v2 | http://arxiv.org/pdf/1806.04820v2.pdf | null | [
"Zexian Zeng",
"Yu Deng",
"Xiaoyu Li",
"Tristan Naumann",
"Yuan Luo"
] | [
"Computational Phenotyping"
] | 2018-06-13T00:00:00 | null | null | null | null | [
{
"code_snippet_url": null,
"description": "Please enter a description about the method here",
"full_name": "Interpretability",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Image Models** are methods that build representations of images f... |
https://paperswithcode.com/paper/to-understand-deep-learning-we-need-to | 1802.01396 | null | null | To understand deep learning we need to understand kernel learning | Generalization performance of classifiers in deep learning has recently
become a subject of intense study. Deep models, typically over-parametrized,
tend to fit the training data exactly. Despite this "overfitting", they perform
well on test data, a phenomenon not yet fully understood.
The first point of our paper is... | null | http://arxiv.org/abs/1802.01396v3 | http://arxiv.org/pdf/1802.01396v3.pdf | ICML 2018 7 | [
"Mikhail Belkin",
"Siyuan Ma",
"Soumik Mandal"
] | [
"Deep Learning",
"Generalization Bounds"
] | 2018-02-05T00:00:00 | https://icml.cc/Conferences/2018/Schedule?showEvent=2026 | http://proceedings.mlr.press/v80/belkin18a/belkin18a.pdf | to-understand-deep-learning-we-need-to-1 | null | [
{
"code_snippet_url": "",
"description": "How Do I Communicate to Expedia?\r\nHow Do I Communicate to Expedia? – Call **☎️ +1-(888) 829 (0881) or +1-805-330-4056 or +1-805-330-4056** for Live Support & Special Travel Discounts!Frustrated with automated systems? Call **☎️ **☎️ +1-(888) 829 (0881) or +1-... |
Subsets and Splits
SQL Console for pwc-archive/papers-with-abstracts
Reveals the frequency of different tasks mentioned in the dataset, highlighting the most common ones.
SQL Console for pwc-archive/papers-with-abstracts
Filters papers containing both "multimodal" and "RAG" terms in their abstracts, helping identify relevant research at the intersection of multimodal retrieval and RAG systems.
SQL Console for pwc-archive/papers-with-abstracts
Retrieves the most recent 100 entries from 2021 to 2025 that mention specific AI-related terms in the title, abstract, short abstract, or tasks, providing a focused view of relevant research topics.
Swarm Intelligence & Optimization Papers
Retrieves up to 100 entries related to 'swarm intelligence' or 'optimization' from the specified date range, providing a focused sample for further analysis.
Filter & Shorten Paper URLs
The query performs basic filtering and formatting, providing shortened URLs and associated ArXiv IDs, which offers limited insight into the dataset.
Filtered Paper URLs and IDs
Provides a limited set of shortened paper URLs and their corresponding arXiv IDs, offering basic filtering and retrieval of specific data points.
Train URLS and ArXiv IDs
Returns the URLs and arXiv IDs of papers where the arXiv ID is available, providing a simple list of paper references.