uid int64 4 318k | paper_url stringlengths 39 81 | arxiv_id stringlengths 9 16 ⌀ | title stringlengths 6 365 | abstract stringlengths 0 7.27k | url_abs stringlengths 17 601 | url_pdf stringlengths 21 819 | proceeding stringlengths 7 1.03k ⌀ | authors list | tasks list | date float64 422B 1,672B ⌀ | methods list | __index_level_0__ int64 1 197k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
172,339 | https://paperswithcode.com/paper/maximum-a-posteriori-signal-recovery-for | 2010.15682 | Maximum a posteriori signal recovery for optical coherence tomography angiography image generation and denoising | Optical coherence tomography angiography (OCTA) is a novel and clinically promising imaging modality to image retinal and sub-retinal vasculature. Based on repeated optical coherence tomography (OCT) scans, intensity changes are observed over time and used to compute OCTA image data. OCTA data are prone to noise and ar... | https://arxiv.org/abs/2010.15682v1 | https://arxiv.org/pdf/2010.15682v1.pdf | null | [
"Lennart Husvogt",
"Stefan B. Ploner",
"Siyu Chen",
"Daniel Stromer",
"Julia Schottenhamml",
"A. Yasin Alibhai",
"Eric Moult",
"Nadia K. Waheed",
"James G. Fujimoto",
"Andreas Maier"
] | [
"Denoising",
"Image Generation"
] | 1,603,929,600,000 | [] | 25,324 |
3,841 | https://paperswithcode.com/paper/code-completion-with-neural-attention-and | 1711.09573 | Code Completion with Neural Attention and Pointer Networks | Intelligent code completion has become an essential research task to
accelerate modern software development. To facilitate effective code completion
for dynamically-typed programming languages, we apply neural language models by
learning from large codebases, and develop a tailored attention mechanism for
code completi... | http://arxiv.org/abs/1711.09573v2 | http://arxiv.org/pdf/1711.09573v2.pdf | null | [
"Jian Li",
"Yue Wang",
"Michael R. Lyu",
"Irwin King"
] | [
"Code Completion"
] | 1,511,740,800,000 | [] | 140,067 |
151,672 | https://paperswithcode.com/paper/naist-s-machine-translation-systems-for-iwslt | null | NAIST's Machine Translation Systems for IWSLT 2020 Conversational Speech Translation Task | This paper describes NAIST{'}s NMT system submitted to the IWSLT 2020 conversational speech translation task. We focus on the translation disfluent speech transcripts that include ASR errors and non-grammatical utterances. We tried a domain adaptation method by transferring the styles of out-of-domain data (United Nati... | https://aclanthology.org/2020.iwslt-1.21 | https://aclanthology.org/2020.iwslt-1.21.pdf | WS 2020 7 | [
"Ryo Fukuda",
"Katsuhito Sudoh",
"Satoshi Nakamura"
] | [
"Domain Adaptation",
"Machine Translation",
"Style Transfer"
] | 1,593,561,600,000 | [] | 124,264 |
124,349 | https://paperswithcode.com/paper/influence-aware-memory-for-deep-reinforcement-1 | 1911.07643 | Influence-aware Memory Architectures for Deep Reinforcement Learning | Due to its perceptual limitations, an agent may have too little information about the state of the environment to act optimally. In such cases, it is important to keep track of the observation history to uncover hidden state. Recent deep reinforcement learning methods use recurrent neural networks (RNN) to memorize pas... | https://arxiv.org/abs/1911.07643v4 | https://arxiv.org/pdf/1911.07643v4.pdf | null | [
"Miguel Suau",
"Jinke He",
"Elena Congeduti",
"Rolf A. N. Starre",
"Aleksander Czechowski",
"Frans A. Oliehoek"
] | [
"reinforcement-learning"
] | 1,574,035,200,000 | [] | 166,238 |
101,001 | https://paperswithcode.com/paper/deep-unified-multimodal-embeddings-for | 1905.07075 | Deep Unified Multimodal Embeddings for Understanding both Content and Users in Social Media Networks | There has been an explosion of multimodal content generated on social media networks in the last few years, which has necessitated a deeper understanding of social media content and user behavior. We present a novel content-independent content-user-reaction model for social multimedia content analysis. Compared to prio... | https://arxiv.org/abs/1905.07075v3 | https://arxiv.org/pdf/1905.07075v3.pdf | null | [
"Karan Sikka",
"Lucas Van Bramer",
"Ajay Divakaran"
] | [
"Cross-Modal Retrieval"
] | 1,558,051,200,000 | [] | 108,730 |
105,815 | https://paperswithcode.com/paper/few-shot-learning-with-per-sample-rich | 1906.03859 | Few-Shot Learning with Per-Sample Rich Supervision | Learning with few samples is a major challenge for parameter-rich models like deep networks. In contrast, people learn complex new concepts even from very few examples, suggesting that the sample complexity of learning can often be reduced. Many approaches to few-shot learning build on transferring a representation fro... | https://arxiv.org/abs/1906.03859v1 | https://arxiv.org/pdf/1906.03859v1.pdf | null | [
"Roman Visotsky",
"Yuval Atzmon",
"Gal Chechik"
] | [
"Few-Shot Learning",
"Classification",
"Meta-Learning",
"Scene Classification"
] | 1,560,124,800,000 | [] | 81,212 |
9,528 | https://paperswithcode.com/paper/constrained-image-generation-using-binarized | 1802.08795 | Constrained Image Generation Using Binarized Neural Networks with Decision Procedures | We consider the problem of binary image generation with given properties.
This problem arises in a number of practical applications, including generation
of artificial porous medium for an electrode of lithium-ion batteries, for
composed materials, etc. A generated image represents a porous medium and, as
such, it is s... | http://arxiv.org/abs/1802.08795v1 | http://arxiv.org/pdf/1802.08795v1.pdf | null | [
"Svyatoslav Korneev",
"Nina Narodytska",
"Luca Pulina",
"Armando Tacchella",
"Nikolaj Bjorner",
"Mooly Sagiv"
] | [
"Image Generation"
] | 1,519,430,400,000 | [] | 121,913 |
63,916 | https://paperswithcode.com/paper/learning-to-predict-denotational | null | Learning to Predict Denotational Probabilities For Modeling Entailment | We propose a framework that captures the denotational probabilities of words and phrases by embedding them in a vector space, and present a method to induce such an embedding from a dataset of denotational probabilities. We show that our model successfully predicts denotational probabilities for unseen phrases, and tha... | https://aclanthology.org/E17-1068 | https://aclanthology.org/E17-1068.pdf | EACL 2017 4 | [
"Alice Lai",
"Julia Hockenmaier"
] | [
"Coreference Resolution",
"Natural Language Inference"
] | 1,491,004,800,000 | [] | 74,007 |
201,003 | https://paperswithcode.com/paper/adversarially-guided-actor-critic-1 | 2102.04376 | Adversarially Guided Actor-Critic | Despite definite success in deep reinforcement learning problems, actor-critic algorithms are still confronted with sample inefficiency in complex environments, particularly in tasks where efficient exploration is a bottleneck. These methods consider a policy (the actor) and a value function (the critic) whose respecti... | https://arxiv.org/abs/2102.04376v1 | https://arxiv.org/pdf/2102.04376v1.pdf | ICLR 2021 1 | [
"Yannis Flet-Berliac",
"Johan Ferret",
"Olivier Pietquin",
"Philippe Preux",
"Matthieu Geist"
] | [
"Efficient Exploration"
] | 1,612,742,400,000 | [] | 50,348 |
75,241 | https://paperswithcode.com/paper/generative-entity-networks-disentangling | null | Generative Entity Networks: Disentangling Entitites and Attributes in Visual Scenes using Partial Natural Language Descriptions | Generative image models have made significant progress in the last few years, and are now able to generate low-resolution images which sometimes look realistic. However the state-of-the-art models utilize fully entangled latent representations where small changes to a single neuron can effect every output pixel in rela... | https://openreview.net/forum?id=BJInMmWC- | https://openreview.net/pdf?id=BJInMmWC- | ICLR 2018 1 | [
"Charlie Nash",
"Sebastian Nowozin",
"Nate Kushman"
] | [
"Question Answering"
] | 1,514,764,800,000 | [
{
"code_snippet_url": "https://github.com/L1aoXingyu/pytorch-beginner/blob/9c86be785c7c318a09cf29112dd1f1a58613239b/08-AutoEncoder/simple_autoencoder.py#L38",
"description": "An **Autoencoder** is a bottleneck architecture that turns a high-dimensional input into a latent low-dimensional code (encoder), and... | 5,299 |
298,219 | https://paperswithcode.com/paper/where-are-my-neighbors-exploiting-patches | 2206.00481 | Where are my Neighbors? Exploiting Patches Relations in Self-Supervised Vision Transformer | Vision Transformers (ViTs) enabled the use of transformer architecture on vision tasks showing impressive performances when trained on big datasets. However, on relatively small datasets, ViTs are less accurate given their lack of inductive bias. To this end, we propose a simple but still effective self-supervised lear... | https://arxiv.org/abs/2206.00481v1 | https://arxiv.org/pdf/2206.00481v1.pdf | null | [
"Guglielmo Camporese",
"Elena Izzo",
"Lamberto Ballan"
] | [
"Inductive Bias",
"Self-Supervised Learning"
] | 1,654,041,600,000 | [] | 192,503 |
197,581 | https://paperswithcode.com/paper/fakebuster-a-deepfakes-detection-tool-for | 2101.03321 | FakeBuster: A DeepFakes Detection Tool for Video Conferencing Scenarios | This paper proposes a new DeepFake detector FakeBuster for detecting impostors during video conferencing and manipulated faces on social media. FakeBuster is a standalone deep learning based solution, which enables a user to detect if another person's video is manipulated or spoofed during a video conferencing based me... | https://arxiv.org/abs/2101.03321v1 | https://arxiv.org/pdf/2101.03321v1.pdf | null | [
"Vineet Mehta",
"Parul Gupta",
"Ramanathan Subramanian",
"Abhinav Dhall"
] | [
"Face Swapping"
] | 1,610,150,400,000 | [] | 5,388 |
168,778 | https://paperswithcode.com/paper/a-deep-learning-based-interactive-sketching | 2010.04413 | A deep learning based interactive sketching system for fashion images design | In this work, we propose an interactive system to design diverse high-quality garment images from fashion sketches and the texture information. The major challenge behind this system is to generate high-quality and detailed texture according to the user-provided texture information. Prior works mainly use the texture p... | https://arxiv.org/abs/2010.04413v1 | https://arxiv.org/pdf/2010.04413v1.pdf | null | [
"Yao Li",
"Xianggang Yu",
"Xiaoguang Han",
"Nianjuan Jiang",
"Kui Jia",
"Jiangbo Lu"
] | [
"Intrinsic Image Decomposition",
"Texture Synthesis"
] | 1,602,201,600,000 | [] | 17,119 |
227,557 | https://paperswithcode.com/paper/reinforcement-learning-based-dialogue-guided | 2106.12384 | Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations | Event extraction is a fundamental task for natural language processing. Finding the roles of event arguments like event participants is essential for event extraction. However, doing so for real-life event descriptions is challenging because an argument's role often varies in different contexts. While the relationship ... | https://arxiv.org/abs/2106.12384v2 | https://arxiv.org/pdf/2106.12384v2.pdf | null | [
"Qian Li",
"Hao Peng",
"JianXin Li",
"Jia Wu",
"Yuanxing Ning",
"Lihong Wang",
"Philip S. Yu",
"Zheng Wang"
] | [
"Event Extraction",
"Incremental Learning",
"reinforcement-learning"
] | 1,624,406,400,000 | [] | 134,800 |
26,039 | https://paperswithcode.com/paper/adversarial-examples-for-generative-models | 1702.06832 | Adversarial examples for generative models | We explore methods of producing adversarial examples on deep generative
models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning
architectures are known to be vulnerable to adversarial examples, but previous
work has focused on the application of adversarial examples to classification
tasks. Deep... | http://arxiv.org/abs/1702.06832v1 | http://arxiv.org/pdf/1702.06832v1.pdf | null | [
"Jernej Kos",
"Ian Fischer",
"Dawn Song"
] | [
"Classification",
"Classification"
] | 1,487,721,600,000 | [
{
"code_snippet_url": "https://github.com/L1aoXingyu/pytorch-beginner/blob/9c86be785c7c318a09cf29112dd1f1a58613239b/08-AutoEncoder/simple_autoencoder.py#L38",
"description": "An **Autoencoder** is a bottleneck architecture that turns a high-dimensional input into a latent low-dimensional code (encoder), and... | 153,759 |
279,975 | https://paperswithcode.com/paper/cake-a-scalable-commonsense-aware-framework | 2202.13785 | CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion | Knowledge graphs store a large number of factual triples while they are still incomplete, inevitably. The previous knowledge graph completion (KGC) models predict missing links between entities merely relying on fact-view data, ignoring the valuable commonsense knowledge. The previous knowledge graph embedding (KGE) te... | https://arxiv.org/abs/2202.13785v3 | https://arxiv.org/pdf/2202.13785v3.pdf | ACL 2022 5 | [
"Guanglin Niu",
"Bo Li",
"Yongfei Zhang",
"ShiLiang Pu"
] | [
"Graph Embedding",
"Knowledge Graph Completion",
"Knowledge Graph Embedding",
"Knowledge Graphs",
"Link Prediction"
] | 1,645,747,200,000 | [] | 53,744 |
184,651 | https://paperswithcode.com/paper/mufold-betaturn-a-deep-dense-inception | 1808.04322 | MUFold-BetaTurn: A Deep Dense Inception Network for Protein Beta-Turn Prediction | Beta-turn prediction is useful in protein function studies and experimental
design. Although recent approaches using machine-learning techniques such as
SVM, neural networks, and K-NN have achieved good results for beta-turn
pre-diction, there is still significant room for improvement. As previous
predictors utilized f... | http://arxiv.org/abs/1808.04322v1 | http://arxiv.org/pdf/1808.04322v1.pdf | null | [] | [
"Experimental Design",
"Feature Engineering"
] | 1,534,118,400,000 | [] | 97,061 |
137,241 | https://paperswithcode.com/paper/pool-based-unsupervised-active-learning-for | 2003.07658 | Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM) | Active learning (AL) selects the most beneficial unlabeled samples to label, and hence a better machine learning model can be trained from the same number of labeled samples. Most existing active learning for regression (ALR) approaches are supervised, which means the sampling process must use some label information, o... | https://arxiv.org/abs/2003.07658v2 | https://arxiv.org/pdf/2003.07658v2.pdf | null | [
"Ziang Liu",
"Xue Jiang",
"Hanbin Luo",
"Weili Fang",
"Jiajing Liu",
"Dongrui Wu"
] | [
"Active Learning"
] | 1,584,403,200,000 | [
{
"code_snippet_url": null,
"description": "**Linear Regression** is a method for modelling a relationship between a dependent variable and independent variables. These models can be fit with numerous approaches. The most common is *least squares*, where we minimize the mean square error between the predict... | 120,211 |
293,867 | https://paperswithcode.com/paper/cross-modal-cloze-task-a-new-task-to-brain-to | null | Cross-Modal Cloze Task: A New Task to Brain-to-Word Decoding | Decoding language from non-invasive brain activity has attracted increasing attention from both researchers in neuroscience and natural language processing. Due to the noisy nature of brain recordings, existing work has simplified brain-to-word decoding as a binary classification task which is to discriminate a brain s... | https://aclanthology.org/2022.findings-acl.54 | https://aclanthology.org/2022.findings-acl.54.pdf | Findings (ACL) 2022 5 | [
"Shuxian Zou",
"Shaonan Wang",
"Jiajun Zhang",
"Chengqing Zong"
] | [
"Language Modelling"
] | 1,651,363,200,000 | [] | 154,832 |
227,847 | https://paperswithcode.com/paper/bayesian-inference-in-high-dimensional-time-1 | 2106.13379 | Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model | Many modern time-series datasets contain large numbers of output response variables sampled for prolonged periods of time. For example, in neuroscience, the activities of 100s-1000's of neurons are recorded during behaviors and in response to sensory stimuli. Multi-output Gaussian process models leverage the nonparamet... | https://arxiv.org/abs/2106.13379v2 | https://arxiv.org/pdf/2106.13379v2.pdf | null | [
"Rui Meng",
"Kristofer Bouchard"
] | [
"Bayesian Inference",
"Gaussian Processes",
"Time Series"
] | 1,624,579,200,000 | [
{
"code_snippet_url": null,
"description": "**Gaussian Processes** are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for an unseen point from training data. The models are fully probabilistic so uncertainty... | 102,352 |
236,184 | https://paperswithcode.com/paper/modulating-language-models-with-emotions | 2108.07886 | Modulating Language Models with Emotions | Generating context-aware language that embodies diverse emotions is an important step towards building empathetic NLP systems. In this paper, we propose a formulation of modulated layer normalization -- a technique inspired by computer vision -- that allows us to use large-scale language models for emotional response g... | https://arxiv.org/abs/2108.07886v1 | https://arxiv.org/pdf/2108.07886v1.pdf | Findings (ACL) 2021 8 | [
"Ruibo Liu",
"Jason Wei",
"Chenyan Jia",
"Soroush Vosoughi"
] | [
"Response Generation"
] | 1,629,158,400,000 | [
{
"code_snippet_url": "https://github.com/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** direct... | 97,900 |
290,977 | https://paperswithcode.com/paper/defending-against-person-hiding-adversarial | 2204.13004 | Defending Against Person Hiding Adversarial Patch Attack with a Universal White Frame | Object detection has attracted great attention in the computer vision area and has emerged as an indispensable component in many vision systems. In the era of deep learning, many high-performance object detection networks have been proposed. Although these detection networks show high performance, they are vulnerable t... | https://arxiv.org/abs/2204.13004v1 | https://arxiv.org/pdf/2204.13004v1.pdf | null | [
"Youngjoon Yu",
"Hong Joo Lee",
"Hakmin Lee",
"Yong Man Ro"
] | [
"Autonomous Driving",
"Object Detection",
"Object Detection"
] | 1,651,017,600,000 | [] | 191,602 |
290,047 | https://paperswithcode.com/paper/towards-fewer-labels-support-pair-active | 2204.10008 | Towards Fewer Labels: Support Pair Active Learning for Person Re-identification | Supervised-learning based person re-identification (re-id) require a large amount of manual labeled data, which is not applicable in practical re-id deployment. In this work, we propose a Support Pair Active Learning (SPAL) framework to lower the manual labeling cost for large-scale person reidentification. The support... | https://arxiv.org/abs/2204.10008v1 | https://arxiv.org/pdf/2204.10008v1.pdf | null | [
"Dapeng Jin",
"Minxian Li"
] | [
"Active Learning",
"Person Re-Identification"
] | 1,650,499,200,000 | [] | 22,530 |
822 | https://paperswithcode.com/paper/addition-of-code-mixed-features-to-enhance | 1806.03821 | Addition of Code Mixed Features to Enhance the Sentiment Prediction of Song Lyrics | Sentiment analysis, also called opinion mining, is the field of study that
analyzes people's opinions,sentiments, attitudes and emotions. Songs are
important to sentiment analysis since the songs and mood are mutually dependent
on each other. Based on the selected song it becomes easy to find the mood of
the listener, ... | http://arxiv.org/abs/1806.03821v1 | http://arxiv.org/pdf/1806.03821v1.pdf | null | [
"Gangula Rama Rohit Reddy",
"Radhika Mamidi"
] | [
"Language Identification",
"Opinion Mining",
"Sentiment Analysis"
] | 1,528,675,200,000 | [] | 174,454 |
6,803 | https://paperswithcode.com/paper/multi-lingual-neural-title-generation-for-e | 1804.01041 | Multi-lingual neural title generation for e-Commerce browse pages | To provide better access of the inventory to buyers and better search engine
optimization, e-Commerce websites are automatically generating millions of
easily searchable browse pages. A browse page consists of a set of slot
name/value pairs within a given category, grouping multiple items which share
some characteristi... | http://arxiv.org/abs/1804.01041v1 | http://arxiv.org/pdf/1804.01041v1.pdf | NAACL 2018 6 | [
"Prashant Mathur",
"Nicola Ueffing",
"Gregor Leusch"
] | [
"Transfer Learning"
] | 1,522,713,600,000 | [] | 185,413 |
193,153 | https://paperswithcode.com/paper/understanding-interpretability-by-generalized | 2012.03089 | Understanding Interpretability by generalized distillation in Supervised Classification | The ability to interpret decisions taken by Machine Learning (ML) models is fundamental to encourage trust and reliability in different practical applications. Recent interpretation strategies focus on human understanding of the underlying decision mechanisms of the complex ML models. However, these strategies are rest... | https://arxiv.org/abs/2012.03089v1 | https://arxiv.org/pdf/2012.03089v1.pdf | null | [
"Adit Agarwal",
"Dr. K. K. Shukla",
"Arjan Kuijper",
"Anirban Mukhopadhyay"
] | [
"Classification",
"Classification"
] | 1,607,126,400,000 | [
{
"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... | 60,649 |
313,207 | https://paperswithcode.com/paper/improving-multilayer-perceptron-mlp-based | 2208.09711 | Improving Multilayer-Perceptron(MLP)-based Network Anomaly Detection with Birch Clustering on CICIDS-2017 Dataset | Machine learning algorithms have been widely used in intrusion detection systems, including Multi-layer Perceptron (MLP). In this study, we proposed a two-stage model that combines the Birch clustering algorithm and MLP classifier to improve the performance of network anomaly multi-classification. In our proposed metho... | https://arxiv.org/abs/2208.09711v1 | https://arxiv.org/pdf/2208.09711v1.pdf | null | [
"Yuhua Yin",
"Julian Jang-Jaccard",
"Fariza Sabrina",
"Jin Kwak"
] | [
"Anomaly Detection",
"Intrusion Detection",
"pseudo label"
] | 1,660,953,600,000 | [
{
"code_snippet_url": "https://cryptoabout.info",
"description": "**k-Means Clustering** is a clustering algorithm that divides a training set into $k$ different clusters of examples that are near each other. It works by initializing $k$ different centroids {$\\mu\\left(1\\right),\\ldots,\\mu\\left(k\\right... | 92,023 |
52,195 | https://paperswithcode.com/paper/twitter-sentiment-analysis-via-bi-sense-emoji | 1807.07961 | Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM | Sentiment analysis on large-scale social media data is important to bridge
the gaps between social media contents and real world activities including
political election prediction, individual and public emotional status
monitoring and analysis, and so on. Although textual sentiment analysis has
been well studied based ... | http://arxiv.org/abs/1807.07961v2 | http://arxiv.org/pdf/1807.07961v2.pdf | null | [
"Yuxiao Chen",
"Jianbo Yuan",
"Quanzeng You",
"Jiebo Luo"
] | [
"Sentiment Analysis",
"Twitter Sentiment Analysis"
] | 1,532,044,800,000 | [
{
"code_snippet_url": "https://github.com/aykutaaykut/Memory-Networks",
"description": "A **Memory Network** provides a memory component that can be read from and written to with the inference capabilities of a neural network model. The motivation is that many neural networks lack a long-term memory compone... | 87,823 |
164,737 | https://paperswithcode.com/paper/an-incentive-mechanism-for-federated-learning | 2009.10269 | An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approach | Federated Learning (FL) is a distributed learning framework that can deal with the distributed issue in machine learning and still guarantee high learning performance. However, it is impractical that all users will sacrifice their resources to join the FL algorithm. This motivates us to study the incentive mechanism de... | https://arxiv.org/abs/2009.10269v1 | https://arxiv.org/pdf/2009.10269v1.pdf | null | [
"Tra Huong Thi Le",
"Nguyen H. Tran",
"Yan Kyaw Tun",
"Minh N. H. Nguyen",
"Shashi Raj Pandey",
"Zhu Han",
"Choong Seon Hong"
] | [
"Federated Learning"
] | 1,600,732,800,000 | [] | 25,683 |
314,754 | https://paperswithcode.com/paper/spoofing-aware-attention-based-asv-back-end | 2209.00423 | Spoofing-Aware Attention based ASV Back-end with Multiple Enrollment Utterances and a Sampling Strategy for the SASV Challenge 2022 | Current state-of-the-art automatic speaker verification (ASV) systems are vulnerable to presentation attacks, and several countermeasures (CMs), which distinguish bona fide trials from spoofing ones, have been explored to protect ASV. However, ASV systems and CMs are generally developed and optimized independently with... | https://arxiv.org/abs/2209.00423v1 | https://arxiv.org/pdf/2209.00423v1.pdf | null | [
"Chang Zeng",
"Lin Zhang",
"Meng Liu",
"Junichi Yamagishi"
] | [
"Speaker Verification"
] | 1,661,990,400,000 | [] | 186,256 |
256,745 | https://paperswithcode.com/paper/parbleu-augmenting-metrics-with-automatic | null | ParBLEU: Augmenting Metrics with Automatic Paraphrases for the WMT’20 Metrics Shared Task | We describe parBLEU, parCHRF++, and parESIM, which augment baseline metrics with automatically generated paraphrases produced by PRISM (Thompson and Post, 2020a), a multilingual neural machine translation system. We build on recent work studying how to improve BLEU by using diverse automatically paraphrased references ... | https://aclanthology.org/2020.wmt-1.98 | https://aclanthology.org/2020.wmt-1.98.pdf | WMT (EMNLP) 2020 11 | [
"Rachel Bawden",
"Biao Zhang",
"Andre Tättar",
"Matt Post"
] | [
"Machine Translation"
] | 1,604,188,800,000 | [] | 32,834 |
207,192 | https://paperswithcode.com/paper/learning-to-simulate-on-sparse-trajectory | 2103.11845 | Learning to Simulate on Sparse Trajectory Data | Simulation of the real-world traffic can be used to help validate the transportation policies. A good simulator means the simulated traffic is similar to real-world traffic, which often requires dense traffic trajectories (i.e., with a high sampling rate) to cover dynamic situations in the real world. However, in most ... | https://arxiv.org/abs/2103.11845v1 | https://arxiv.org/pdf/2103.11845v1.pdf | null | [
"Hua Wei",
"Chacha Chen",
"Chang Liu",
"Guanjie Zheng",
"Zhenhui Li"
] | [
"Imitation Learning"
] | 1,616,371,200,000 | [] | 148,197 |
13,588 | https://paperswithcode.com/paper/a-variational-approach-to-shape-from-shading | 1709.10354 | A Variational Approach to Shape-from-shading Under Natural Illumination | A numerical solution to shape-from-shading under natural illumination is
presented. It builds upon an augmented Lagrangian approach for solving a
generic PDE-based shape-from-shading model which handles directional or
spherical harmonic lighting, orthographic or perspective projection, and
greylevel or multi-channel im... | http://arxiv.org/abs/1709.10354v2 | http://arxiv.org/pdf/1709.10354v2.pdf | null | [
"Yvain Quéau",
"Jean Mélou",
"Fabien Castan",
"Daniel Cremers",
"Jean-Denis Durou"
] | [
"Denoising"
] | 1,506,643,200,000 | [] | 131,612 |
212,741 | https://paperswithcode.com/paper/unsupervised-learning-of-explainable-parse | 2104.04998 | Unsupervised Learning of Explainable Parse Trees for Improved Generalisation | Recursive neural networks (RvNN) have been shown useful for learning sentence representations and helped achieve competitive performance on several natural language inference tasks. However, recent RvNN-based models fail to learn simple grammar and meaningful semantics in their intermediate tree representation. In this... | https://arxiv.org/abs/2104.04998v1 | https://arxiv.org/pdf/2104.04998v1.pdf | null | [
"Atul Sahay",
"Ayush Maheshwari",
"Ritesh Kumar",
"Ganesh Ramakrishnan",
"Manjesh Kumar Hanawal",
"Kavi Arya"
] | [
"Natural Language Inference",
"Sentiment Analysis"
] | 1,618,099,200,000 | [] | 137,812 |
277,335 | https://paperswithcode.com/paper/towards-weakly-supervised-text-spotting-using | 2202.05508 | Towards Weakly-Supervised Text Spotting using a Multi-Task Transformer | Text spotting end-to-end methods have recently gained attention in the literature due to the benefits of jointly optimizing the text detection and recognition components. Existing methods usually have a distinct separation between the detection and recognition branches, requiring exact annotations for the two tasks. We... | https://arxiv.org/abs/2202.05508v2 | https://arxiv.org/pdf/2202.05508v2.pdf | CVPR 2022 1 | [
"Yair Kittenplon",
"Inbal Lavi",
"Sharon Fogel",
"Yarin Bar",
"R. Manmatha",
"Pietro Perona"
] | [
"Text Spotting"
] | 1,644,537,600,000 | [] | 6,532 |
168,919 | https://paperswithcode.com/paper/a-novel-strategy-for-covid-19-classification | 2010.05690 | COVID-19 Classification Using Staked Ensembles: A Comprehensive Analysis | The issue of COVID-19, increasing with a massive mortality rate. This led to the WHO declaring it as a pandemic. In this situation, it is crucial to perform efficient and fast diagnosis. The reverse transcript polymerase chain reaction (RTPCR) test is conducted to detect the presence of SARS-CoV-2. This test is time-co... | https://arxiv.org/abs/2010.05690v3 | https://arxiv.org/pdf/2010.05690v3.pdf | null | [
"Lalith Bharadwaj B",
"Rohit Boddeda",
"Sai Vardhan K",
"Madhu G"
] | [
"Classification"
] | 1,602,028,800,000 | [] | 2,990 |
264,422 | https://paperswithcode.com/paper/multilingual-pre-training-with-language-and | null | Multilingual pre-training with Language and Task Adaptation for Multilingual Text Style Transfer | We exploit the pre-trained seq2seq model mBART for multilingual text style transfer. Using machine translated data as well as gold aligned English sentences yields state-of-the-art results in the three target languages we consider. Besides, in view of the general scarcity of parallel data, we propose a modular approac... | https://openreview.net/forum?id=rWPLdCIiY6g | https://openreview.net/pdf?id=rWPLdCIiY6g | ACL ARR November 2021 11 | [
"Anonymous"
] | [
"Style Transfer",
"Text Style Transfer"
] | 1,637,020,800,000 | [
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L329",
"description": "**Tanh Activation** is an activation function used for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\r\n\r\nH... | 2,148 |
215,525 | https://paperswithcode.com/paper/discovering-an-aid-policy-to-minimize-student | 2104.10258 | Discovering an Aid Policy to Minimize Student Evasion Using Offline Reinforcement Learning | High dropout rates in tertiary education expose a lack of efficiency that causes frustration of expectations and financial waste. Predicting students at risk is not enough to avoid student dropout. Usually, an appropriate aid action must be discovered and applied in the proper time for each student. To tackle this sequ... | https://arxiv.org/abs/2104.10258v1 | https://arxiv.org/pdf/2104.10258v1.pdf | null | [
"Leandro M. de Lima",
"Renato A. Krohling"
] | [
"reinforcement-learning"
] | 1,618,876,800,000 | [
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (... | 77,804 |
8,616 | https://paperswithcode.com/paper/learning-approximate-inference-networks-for | 1803.03376 | Learning Approximate Inference Networks for Structured Prediction | Structured prediction energy networks (SPENs; Belanger & McCallum 2016) use
neural network architectures to define energy functions that can capture
arbitrary dependencies among parts of structured outputs. Prior work used
gradient descent for inference, relaxing the structured output to a set of
continuous variables a... | http://arxiv.org/abs/1803.03376v1 | http://arxiv.org/pdf/1803.03376v1.pdf | ICLR 2018 1 | [
"Lifu Tu",
"Kevin Gimpel"
] | [
"Language Modelling",
"Multi-Label Classification",
"Part-Of-Speech Tagging",
"Structured Prediction"
] | 1,520,553,600,000 | [] | 56,649 |
221,481 | https://paperswithcode.com/paper/stytr-2-unbiased-image-style-transfer-with | 2105.14576 | StyTr$^2$: Image Style Transfer with Transformers | The goal of image style transfer is to render an image with artistic features guided by a style reference while maintaining the original content. Owing to the locality in convolutional neural networks (CNNs), extracting and maintaining the global information of input images is difficult. Therefore, traditional neural s... | https://arxiv.org/abs/2105.14576v3 | https://arxiv.org/pdf/2105.14576v3.pdf | null | [
"Yingying Deng",
"Fan Tang",
"WeiMing Dong",
"Chongyang Ma",
"Xingjia Pan",
"Lei Wang",
"Changsheng Xu"
] | [
"Style Transfer"
] | 1,622,332,800,000 | [] | 130,489 |
206,830 | https://paperswithcode.com/paper/consistency-based-active-learning-for-object | 2103.10374 | Consistency-based Active Learning for Object Detection | Active learning aims to improve the performance of task model by selecting the most informative samples with a limited budget. Unlike most recent works that focused on applying active learning for image classification, we propose an effective Consistency-based Active Learning method for object Detection (CALD), which f... | https://arxiv.org/abs/2103.10374v3 | https://arxiv.org/pdf/2103.10374v3.pdf | null | [
"Weiping Yu",
"Sijie Zhu",
"Taojiannan Yang",
"Chen Chen"
] | [
"Active Learning",
"Classification",
"Classification",
"Image Classification",
"Object Detection",
"Object Detection"
] | 1,616,025,600,000 | [
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/5e9ebe8dadc0ea2841a46cfcd82a93b4ce0d4519/torchvision/ops/roi_pool.py#L10",
"description": "**Region of Interest Pooling**, or **RoIPool**, is an operation for extracting a small feature map (e.g., $7×7$) from each RoI in detection and segmentatio... | 62,718 |
52,784 | https://paperswithcode.com/paper/news-session-based-recommendations-using-deep | 1808.00076 | News Session-Based Recommendations using Deep Neural Networks | News recommender systems are aimed to personalize users experiences and help
them to discover relevant articles from a large and dynamic search space.
Therefore, news domain is a challenging scenario for recommendations, due to
its sparse user profiling, fast growing number of items, accelerated item's
value decay, and... | http://arxiv.org/abs/1808.00076v3 | http://arxiv.org/pdf/1808.00076v3.pdf | null | [
"Gabriel de Souza P. Moreira",
"Felipe Ferreira",
"Adilson Marques da Cunha"
] | [
"News Recommendation",
"Recommendation Systems",
"Session-Based Recommendations"
] | 1,532,995,200,000 | [] | 166,734 |
254,403 | https://paperswithcode.com/paper/are-factuality-checkers-reliable-adversarial | null | Are Factuality Checkers Reliable? Adversarial Meta-evaluation of Factuality in Summarization | With the continuous upgrading of the summarization systems driven by deep neural networks, researchers have higher requirements on the quality of the generated summaries, which should be not only fluent and informative but also factually correct. As a result, the field of factual evaluation has developed rapidly recent... | https://aclanthology.org/2021.findings-emnlp.179 | https://aclanthology.org/2021.findings-emnlp.179.pdf | Findings (EMNLP) 2021 11 | [
"Yiran Chen",
"PengFei Liu",
"Xipeng Qiu"
] | [
"Data Augmentation"
] | 1,635,724,800,000 | [] | 110,904 |
169,201 | https://paperswithcode.com/paper/block-term-tensor-neural-networks | 2010.04963 | Block-term Tensor Neural Networks | Deep neural networks (DNNs) have achieved outstanding performance in a wide range of applications, e.g., image classification, natural language processing, etc. Despite the good performance, the huge number of parameters in DNNs brings challenges to efficient training of DNNs and also their deployment in low-end device... | https://arxiv.org/abs/2010.04963v2 | https://arxiv.org/pdf/2010.04963v2.pdf | null | [
"Jinmian Ye",
"Guangxi Li",
"Di Chen",
"Haiqin Yang",
"Shandian Zhe",
"Zenglin Xu"
] | [
"Image Classification"
] | 1,602,288,000,000 | [] | 150,066 |
244,768 | https://paperswithcode.com/paper/aggregation-with-feature-detection | null | Aggregation With Feature Detection | Aggregating features from different depths of a network is widely adopted to improve the network capability. Lots of modern architectures are equipped with skip connections, which actually makes the feature aggregation happen in all these networks. Since different features tell different semantic meanings, there a... | http://openaccess.thecvf.com//content/ICCV2021/html/Sun_Aggregation_With_Feature_Detection_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Sun_Aggregation_With_Feature_Detection_ICCV_2021_paper.pdf | ICCV 2021 10 | [
"Shuyang Sun",
"Xiaoyu Yue",
"Xiaojuan Qi",
"Wanli Ouyang",
"Victor Adrian Prisacariu",
"Philip H.S. Torr"
] | [
"Instance Segmentation",
"Object Detection",
"Object Detection",
"Semantic Segmentation"
] | 1,609,459,200,000 | [
{
"code_snippet_url": "",
"description": "**Average Pooling** is a pooling operation that calculates the average value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - me... | 39,332 |
186,426 | https://paperswithcode.com/paper/towards-adversarial-learning-of-speaker | 1903.09606 | Towards adversarial learning of speaker-invariant representation for speech emotion recognition | Speech emotion recognition (SER) has attracted great attention in recent
years due to the high demand for emotionally intelligent speech interfaces.
Deriving speaker-invariant representations for speech emotion recognition is
crucial. In this paper, we propose to apply adversarial training to SER to
learn speaker-invar... | http://arxiv.org/abs/1903.09606v1 | http://arxiv.org/pdf/1903.09606v1.pdf | null | [] | [
"Classification",
"Emotion Classification",
"Emotion Recognition",
"Representation Learning",
"Speech Emotion Recognition"
] | 1,553,212,800,000 | [] | 91,257 |
110,612 | https://paperswithcode.com/paper/chinese-relation-extraction-with-multi | null | Chinese Relation Extraction with Multi-Grained Information and External Linguistic Knowledge | Chinese relation extraction is conducted using neural networks with either character-based or word-based inputs, and most existing methods typically suffer from segmentation errors and ambiguity of polysemy. To address the issues, we propose a multi-grained lattice framework (MG lattice) for Chinese relation extraction... | https://aclanthology.org/P19-1430 | https://aclanthology.org/P19-1430.pdf | ACL 2019 7 | [
"Ziran Li",
"Ning Ding",
"Zhiyuan Liu",
"Hai-Tao Zheng",
"Ying Shen"
] | [
"Relation Extraction"
] | 1,561,939,200,000 | [] | 122,862 |
98,124 | https://paperswithcode.com/paper/transformable-bottleneck-networks | 1904.06458 | Transformable Bottleneck Networks | We propose a novel approach to performing fine-grained 3D manipulation of image content via a convolutional neural network, which we call the Transformable Bottleneck Network (TBN). It applies given spatial transformations directly to a volumetric bottleneck within our encoder-bottleneck-decoder architecture. Multi-vie... | https://arxiv.org/abs/1904.06458v5 | https://arxiv.org/pdf/1904.06458v5.pdf | ICCV 2019 10 | [
"Kyle Olszewski",
"Sergey Tulyakov",
"Oliver Woodford",
"Hao Li",
"Linjie Luo"
] | [
"3D Reconstruction",
"Novel View Synthesis"
] | 1,555,113,600,000 | [] | 120,802 |
107,961 | https://paperswithcode.com/paper/volmap-a-real-time-model-for-semantic | 1906.11873 | VolMap: A Real-time Model for Semantic Segmentation of a LiDAR surrounding view | This paper introduces VolMap, a real-time approach for the semantic segmentation of a 3D LiDAR surrounding view system in autonomous vehicles. We designed an optimized deep convolution neural network that can accurately segment the point cloud produced by a 360\degree{} LiDAR setup, where the input consists of a volume... | https://arxiv.org/abs/1906.11873v1 | https://arxiv.org/pdf/1906.11873v1.pdf | null | [
"Hager Radi",
"Waleed Ali"
] | [
"3D Object Detection",
"Autonomous Vehicles",
"Object Detection",
"Object Detection",
"Semantic Segmentation"
] | 1,560,297,600,000 | [
{
"code_snippet_url": null,
"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,... | 71,378 |
123,059 | https://paperswithcode.com/paper/using-dynamic-embeddings-to-improve-static | 1911.02929 | How Can BERT Help Lexical Semantics Tasks? | Contextualized embeddings such as BERT can serve as strong input representations to NLP tasks, outperforming their static embeddings counterparts such as skip-gram, CBOW and GloVe. However, such embeddings are dynamic, calculated according to a sentence-level context, which limits their use in lexical semantics tasks. ... | https://arxiv.org/abs/1911.02929v2 | https://arxiv.org/pdf/1911.02929v2.pdf | null | [
"Yile Wang",
"Leyang Cui",
"Yue Zhang"
] | [
"Word Embeddings"
] | 1,573,084,800,000 | [
{
"code_snippet_url": "",
"description": "**GloVe Embeddings** are a type of word embedding that encode the co-occurrence probability ratio between two words as vector differences. GloVe uses a weighted least squares objective $J$ that minimizes the difference between the dot product of the vectors of two w... | 135,696 |
307,643 | https://paperswithcode.com/paper/funqg-molecular-representation-learning-via | 2207.08597 | FunQG: Molecular Representation Learning Via Quotient Graphs | Learning expressive molecular representations is crucial to facilitate the accurate prediction of molecular properties. Despite the significant advancement of graph neural networks (GNNs) in molecular representation learning, they generally face limitations such as neighbors-explosion, under-reaching, over-smoothing, a... | https://arxiv.org/abs/2207.08597v1 | https://arxiv.org/pdf/2207.08597v1.pdf | null | [
"Hossein Hajiabolhassan",
"Zahra Taheri",
"Ali Hojatnia",
"Yavar Taheri Yeganeh"
] | [
"Molecular Property Prediction",
"Representation Learning"
] | 1,658,102,400,000 | [] | 54,202 |
182,790 | https://paperswithcode.com/paper/mosaicked-multispectral-image-compression | 1801.03577 | Mosaicked multispectral image compression based on inter- and intra-band correlation | Multispectral imaging has been utilized in many fields, but the cost of
capturing and storing image data is still high. Single-sensor cameras with
multispectral filter arrays can reduce the cost of capturing images at the
expense of slightly lower image quality. When multispectral filter arrays are
used, conventional m... | http://arxiv.org/abs/1801.03577v1 | http://arxiv.org/pdf/1801.03577v1.pdf | null | [] | [
"Image Compression"
] | 1,515,542,400,000 | [] | 149,774 |
98,226 | https://paperswithcode.com/paper/swtvm-exploring-the-automated-compilation-for | 1904.07404 | swTVM: Towards Optimized Tensor Code Generation for Deep Learning on Sunway Many-Core Processor | The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability. Among the existing deep learning compilers, TVM is well known for its efficiency in code generation and optimi... | https://arxiv.org/abs/1904.07404v3 | https://arxiv.org/pdf/1904.07404v3.pdf | null | [
"Mingzhen Li",
"Changxi Liu",
"Jianjin Liao",
"Xuegui Zheng",
"Hailong Yang",
"Rujun Sun",
"Jun Xu",
"Lin Gan",
"Guangwen Yang",
"Zhongzhi Luan",
"Depei Qian"
] | [
"Code Generation"
] | 1,555,372,800,000 | [
{
"code_snippet_url": "https://www.healthnutra.org/es/maxup/",
"description": "A **1 x 1 Convolution** is a [convolution](https://paperswithcode.com/method/convolution) with some special properties in that it can be used for dimensionality reduction, efficient low dimensional embeddings, and applying non-li... | 62,359 |
197,959 | https://paperswithcode.com/paper/instantaneous-psd-estimation-for-speech | 2007.00542 | Instantaneous PSD Estimation for Speech Enhancement based on Generalized Principal Components | Power spectral density (PSD) estimates of various microphone signal components are essential to many speech enhancement procedures. As speech is highly non-nonstationary, performance improvements may be gained by maintaining time-variations in PSD estimates. In this paper, we propose an instantaneous PSD estimation app... | https://arxiv.org/abs/2007.00542v1 | https://arxiv.org/pdf/2007.00542v1.pdf | null | [] | [
"Speech Enhancement"
] | 1,593,561,600,000 | [] | 166,124 |
300,148 | https://paperswithcode.com/paper/transformer-based-urdu-handwritten-text | 2206.04575 | Transformer based Urdu Handwritten Text Optical Character Reader | Extracting Handwritten text is one of the most important components of digitizing information and making it available for large scale setting. Handwriting Optical Character Reader (OCR) is a research problem in computer vision and natural language processing computing, and a lot of work has been done for English, but u... | https://arxiv.org/abs/2206.04575v1 | https://arxiv.org/pdf/2206.04575v1.pdf | null | [
"Mohammad Daniyal Shaiq",
"Musa Dildar Ahmed Cheema",
"Ali Kamal"
] | [
"Natural Language Understanding",
"Optical Character Recognition"
] | 1,654,732,800,000 | [] | 884 |
308,450 | https://paperswithcode.com/paper/revealing-secrets-from-pre-trained-models | 2207.09539 | Revealing Secrets From Pre-trained Models | With the growing burden of training deep learning models with large data sets, transfer-learning has been widely adopted in many emerging deep learning algorithms. Transformer models such as BERT are the main player in natural language processing and use transfer-learning as a de facto standard training method. A few b... | https://arxiv.org/abs/2207.09539v1 | https://arxiv.org/pdf/2207.09539v1.pdf | null | [
"Mujahid Al Rafi",
"Yuan Feng",
"Hyeran Jeon"
] | [
"Model extraction",
"Transfer Learning"
] | 1,658,188,800,000 | [
{
"code_snippet_url": "",
"description": "**Absolute Position Encodings** are a type of position embeddings for [[Transformer](https://paperswithcode.com/method/transformer)-based models] where positional encodings are added to the input embeddings at the bottoms of the encoder and decoder stacks. The posit... | 135,419 |
207,542 | https://paperswithcode.com/paper/watermark-faker-towards-forgery-of-digital | 2103.12489 | Watermark Faker: Towards Forgery of Digital Image Watermarking | Digital watermarking has been widely used to protect the copyright and integrity of multimedia data. Previous studies mainly focus on designing watermarking techniques that are robust to attacks of destroying the embedded watermarks. However, the emerging deep learning based image generation technology raises new open ... | https://arxiv.org/abs/2103.12489v1 | https://arxiv.org/pdf/2103.12489v1.pdf | null | [
"Ruowei Wang",
"Chenguo Lin",
"Qijun Zhao",
"Feiyu Zhu"
] | [
"Image Generation"
] | 1,616,457,600,000 | [
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/7c077f6a986f05383bcb86b535aedb5a63dd5c4b/torchvision/models/densenet.py#L113",
"description": "A **Concatenated Skip Connection** is a type of skip connection that seeks to reuse features by concatenating them to new layers, allowing more informa... | 136,527 |
266,244 | https://paperswithcode.com/paper/transzero-attribute-guided-transformer-for | 2112.01683 | TransZero: Attribute-guided Transformer for Zero-Shot Learning | Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen ones. Semantic knowledge is learned from attribute descriptions shared between different classes, which act as strong priors for localizing object attributes that represent discriminative region featu... | https://arxiv.org/abs/2112.01683v1 | https://arxiv.org/pdf/2112.01683v1.pdf | null | [
"Shiming Chen",
"Ziming Hong",
"Yang Liu",
"Guo-Sen Xie",
"Baigui Sun",
"Hao Li",
"Qinmu Peng",
"Ke Lu",
"Xinge You"
] | [
"Zero-Shot Learning"
] | 1,638,489,600,000 | [
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/b7bda236d18815052378c88081f64935427d7716/torch/optim/adam.py#L6",
"description": "**Adam** is an adaptive learning rate optimization algorithm that utilises both momentum and scaling, combining the benefits of [RMSProp](https://paperswithcode.co... | 159,757 |
154,284 | https://paperswithcode.com/paper/policy-learning-with-partial-observation-and | 2007.03155 | Policy learning with partial observation and mechanical constraints for multi-person modeling | Extracting the rules of real-world biological multi-agent behaviors is a current challenge in various scientific and engineering fields. Biological agents generally have limited observation and mechanical constraints; however, most of the conventional data-driven models ignore such assumptions, resulting in lack of bio... | https://arxiv.org/abs/2007.03155v1 | https://arxiv.org/pdf/2007.03155v1.pdf | null | [
"Keisuke Fujii",
"Naoya Takeishi",
"Yoshinobu Kawahara",
"Kazuya Takeda"
] | [
"Imitation Learning"
] | 1,594,080,000,000 | [
{
"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... | 184,546 |
56,292 | https://paperswithcode.com/paper/emi-exploration-with-mutual-information | 1810.01176 | EMI: Exploration with Mutual Information | Reinforcement learning algorithms struggle when the reward signal is very sparse. In these cases, naive random exploration methods essentially rely on a random walk to stumble onto a rewarding state. Recent works utilize intrinsic motivation to guide the exploration via generative models, predictive forward models, or ... | https://arxiv.org/abs/1810.01176v6 | https://arxiv.org/pdf/1810.01176v6.pdf | null | [
"Hyoungseok Kim",
"Jaekyeom Kim",
"Yeonwoo Jeong",
"Sergey Levine",
"Hyun Oh Song"
] | [
"Continuous Control"
] | 1,538,438,400,000 | [] | 9,889 |
253,027 | https://paperswithcode.com/paper/embedding-structured-dictionary-entries | null | Embedding Structured Dictionary Entries | Previous work has shown how to effectively use external resources such as dictionaries to improve English-language word embeddings, either by manipulating the training process or by applying post-hoc adjustments to the embedding space. We experiment with a multi-task learning approach for explicitly incorporating the s... | https://aclanthology.org/2020.insights-1.18 | https://aclanthology.org/2020.insights-1.18.pdf | EMNLP (insights) 2020 11 | [
"Steven Wilson",
"Walid Magdy",
"Barbara McGillivray",
"Gareth Tyson"
] | [
"Learning Word Embeddings",
"Multi-Task Learning",
"Word Embeddings"
] | 1,604,188,800,000 | [] | 129,159 |
50,499 | https://paperswithcode.com/paper/evaluating-gammatone-frequency-cepstral | 1806.09010 | Evaluating Gammatone Frequency Cepstral Coefficients with Neural Networks for Emotion Recognition from Speech | Current approaches to speech emotion recognition focus on speech features
that can capture the emotional content of a speech signal. Mel Frequency
Cepstral Coefficients (MFCCs) are one of the most commonly used representations
for audio speech recognition and classification. This paper proposes Gammatone
Frequency Ceps... | http://arxiv.org/abs/1806.09010v1 | http://arxiv.org/pdf/1806.09010v1.pdf | null | [
"Gabrielle K. Liu"
] | [
"Classification",
"Emotion Recognition",
"Classification",
"Speech Emotion Recognition",
"Speech Recognition",
"Speech Recognition"
] | 1,529,712,000,000 | [] | 179,379 |
267,412 | https://paperswithcode.com/paper/semantic-search-as-extractive-paraphrase-span-1 | 2112.04886 | Semantic Search as Extractive Paraphrase Span Detection | In this paper, we approach the problem of semantic search by framing the search task as paraphrase span detection, i.e. given a segment of text as a query phrase, the task is to identify its paraphrase in a given document, the same modelling setup as typically used in extractive question answering. On the Turku Paraphr... | https://arxiv.org/abs/2112.04886v1 | https://arxiv.org/pdf/2112.04886v1.pdf | null | [
"Jenna Kanerva",
"Hanna Kitti",
"Li-Hsin Chang",
"Teemu Vahtola",
"Mathias Creutz",
"Filip Ginter"
] | [
"Extractive Question-Answering",
"Question Answering",
"Sentence Embedding",
"Sentence Similarity"
] | 1,639,008,000,000 | [
{
"code_snippet_url": "https://github.com/huggingface/transformers/blob/4dc65591b5c61d75c3ef3a2a883bf1433e08fc45/src/transformers/modeling_tf_bert.py#L271",
"description": "**Attention Dropout** is a type of [dropout](https://paperswithcode.com/method/dropout) used in attention-based architectures, where el... | 162,770 |
545 | https://paperswithcode.com/paper/unsupervised-adaptation-with-interpretable | 1806.04872 | Unsupervised Adaptation with Interpretable Disentangled Representations for Distant Conversational Speech Recognition | The current trend in automatic speech recognition is to leverage large
amounts of labeled data to train supervised neural network models.
Unfortunately, obtaining data for a wide range of domains to train robust
models can be costly. However, it is relatively inexpensive to collect large
amounts of unlabeled data from ... | http://arxiv.org/abs/1806.04872v1 | http://arxiv.org/pdf/1806.04872v1.pdf | null | [
"Wei-Ning Hsu",
"Hao Tang",
"James Glass"
] | [
"Automatic Speech Recognition",
"Speech Recognition",
"Speech Recognition"
] | 1,528,848,000,000 | [] | 54,322 |
94,196 | https://paperswithcode.com/paper/dpod-dense-6d-pose-object-detector-in-rgb | 1902.11020 | DPOD: 6D Pose Object Detector and Refiner | In this paper we present a novel deep learning method for 3D object detection and 6D pose estimation from RGB images. Our method, named DPOD (Dense Pose Object Detector), estimates dense multi-class 2D-3D correspondence maps between an input image and available 3D models. Given the correspondences, a 6DoF pose is compu... | https://arxiv.org/abs/1902.11020v3 | https://arxiv.org/pdf/1902.11020v3.pdf | ICCV 2019 10 | [
"Sergey Zakharov",
"Ivan Shugurov",
"Slobodan Ilic"
] | [
"3D Object Detection",
"6D Pose Estimation",
"6D Pose Estimation using RGB",
"Object Detection",
"Object Detection",
"Pose Estimation"
] | 1,551,312,000,000 | [] | 148,709 |
296,006 | https://paperswithcode.com/paper/supporting-vision-language-model-inference | 2205.11100 | Supporting Vision-Language Model Inference with Causality-pruning Knowledge Prompt | Vision-language models are pre-trained by aligning image-text pairs in a common space so that the models can deal with open-set visual concepts by learning semantic information from textual labels. To boost the transferability of these models on downstream tasks in a zero-shot manner, recent works explore generating fi... | https://arxiv.org/abs/2205.11100v1 | https://arxiv.org/pdf/2205.11100v1.pdf | null | [
"Jiangmeng Li",
"Wenyi Mo",
"Wenwen Qiang",
"Bing Su",
"Changwen Zheng"
] | [
"Domain Generalization",
"Language Modelling"
] | 1,653,264,000,000 | [] | 115,371 |
4,685 | https://paperswithcode.com/paper/an-interactive-greedy-approach-to-group | 1707.02963 | An Interactive Greedy Approach to Group Sparsity in High Dimensions | Sparsity learning with known grouping structure has received considerable
attention due to wide modern applications in high-dimensional data analysis.
Although advantages of using group information have been well-studied by
shrinkage-based approaches, benefits of group sparsity have not been
well-documented for greedy-... | http://arxiv.org/abs/1707.02963v5 | http://arxiv.org/pdf/1707.02963v5.pdf | null | [
"Wei Qian",
"Wending Li",
"Yasuhiro Sogawa",
"Ryohei Fujimaki",
"Xitong Yang",
"Ji Liu"
] | [
"Activity Recognition",
"Human Activity Recognition"
] | 1,499,644,800,000 | [] | 147,498 |
137,985 | https://paperswithcode.com/paper/deep-local-shapes-learning-local-sdf-priors | 2003.10983 | Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction | Efficiently reconstructing complex and intricate surfaces at scale is a long-standing goal in machine perception. To address this problem we introduce Deep Local Shapes (DeepLS), a deep shape representation that enables encoding and reconstruction of high-quality 3D shapes without prohibitive memory requirements. DeepL... | https://arxiv.org/abs/2003.10983v3 | https://arxiv.org/pdf/2003.10983v3.pdf | ECCV 2020 8 | [
"Rohan Chabra",
"Jan Eric Lenssen",
"Eddy Ilg",
"Tanner Schmidt",
"Julian Straub",
"Steven Lovegrove",
"Richard Newcombe"
] | [
"3D Reconstruction",
"Surface Reconstruction"
] | 1,585,008,000,000 | [] | 189,020 |
65,872 | https://paperswithcode.com/paper/knowledge-graph-embedding-with-numeric | null | Knowledge Graph Embedding with Numeric Attributes of Entities | Knowledge Graph (KG) embedding projects entities and relations into low dimensional vector space, which has been successfully applied in KG completion task. The previous embedding approaches only model entities and their relations, ignoring a large number of entities{'} numeric attributes in KGs. In this paper, we prop... | https://aclanthology.org/W18-3017 | https://aclanthology.org/W18-3017.pdf | WS 2018 7 | [
"Yanrong Wu",
"Zhichun Wang"
] | [
"Graph Embedding",
"Knowledge Graph Embedding",
"Knowledge Graphs",
"Link Prediction",
"Representation Learning"
] | 1,530,403,200,000 | [] | 43,180 |
70,781 | https://paperswithcode.com/paper/gated-recurrent-convolution-neural-network | null | Gated Recurrent Convolution Neural Network for OCR | Optical Character Recognition (OCR) aims to recognize text in natural images. Inspired by a recently proposed model for general image classification, Recurrent Convolution Neural Network (RCNN), we propose a new architecture named Gated RCNN (GRCNN) for solving this problem. Its critical component, Gated Recurrent Conv... | http://papers.nips.cc/paper/6637-gated-recurrent-convolution-neural-network-for-ocr | http://papers.nips.cc/paper/6637-gated-recurrent-convolution-neural-network-for-ocr.pdf | NeurIPS 2017 12 | [
"Jianfeng Wang",
"Xiaolin Hu"
] | [
"Classification",
"Image Classification",
"Optical Character Recognition"
] | 1,512,086,400,000 | [
{
"code_snippet_url": null,
"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,... | 131,840 |
100,745 | https://paperswithcode.com/paper/task-driven-modular-networks-for-zero-shot | 1905.05908 | Task-Driven Modular Networks for Zero-Shot Compositional Learning | One of the hallmarks of human intelligence is the ability to compose learned knowledge into novel concepts which can be recognized without a single training example. In contrast, current state-of-the-art methods require hundreds of training examples for each possible category to build reliable and accurate classifiers.... | https://arxiv.org/abs/1905.05908v1 | https://arxiv.org/pdf/1905.05908v1.pdf | ICCV 2019 10 | [
"Senthil Purushwalkam",
"Maximilian Nickel",
"Abhinav Gupta",
"Marc'Aurelio Ranzato"
] | [
"Novel Concepts",
"Zero-Shot Learning"
] | 1,557,878,400,000 | [] | 11,391 |
54,942 | https://paperswithcode.com/paper/deep-mr-image-super-resolution-using | 1809.03140 | Deep MR Image Super-Resolution Using Structural Priors | High resolution magnetic resonance (MR) images are desired for accurate
diagnostics. In practice, image resolution is restricted by factors like
hardware, cost and processing constraints. Recently, deep learning methods have
been shown to produce compelling state of the art results for image
super-resolution. Paying pa... | http://arxiv.org/abs/1809.03140v1 | http://arxiv.org/pdf/1809.03140v1.pdf | null | [
"Venkateswararao Cherukuri",
"Tiantong Guo",
"Steven J. Schiff",
"Vishal Monga"
] | [
"Image Super-Resolution",
"Super-Resolution"
] | 1,536,537,600,000 | [] | 43,546 |
226,931 | https://paperswithcode.com/paper/multi-contextual-design-of-convolutional | 2106.10430 | Multi-Contextual Design of Convolutional Neural Network for Steganalysis | In recent times, deep learning-based steganalysis classifiers became popular due to their state-of-the-art performance. Most deep steganalysis classifiers usually extract noise residuals using high-pass filters as preprocessing steps and feed them to their deep model for classification. It is observed that recent stega... | https://arxiv.org/abs/2106.10430v2 | https://arxiv.org/pdf/2106.10430v2.pdf | null | [
"Brijesh Singh",
"Arijit Sur",
"Pinaki Mitra"
] | [
"Denoising"
] | 1,624,060,800,000 | [] | 170,107 |
277,745 | https://paperswithcode.com/paper/bifsmn-binary-neural-network-for-keyword | 2202.06483 | BiFSMN: Binary Neural Network for Keyword Spotting | The deep neural networks, such as the Deep-FSMN, have been widely studied for keyword spotting (KWS) applications. However, computational resources for these networks are significantly constrained since they usually run on-call on edge devices. In this paper, we present BiFSMN, an accurate and extreme-efficient binary ... | https://arxiv.org/abs/2202.06483v4 | https://arxiv.org/pdf/2202.06483v4.pdf | null | [
"Haotong Qin",
"Xudong Ma",
"Yifu Ding",
"Xiaoyang Li",
"Yang Zhang",
"Yao Tian",
"Zejun Ma",
"Jie Luo",
"Xianglong Liu"
] | [
"Binarization",
"Keyword Spotting"
] | 1,644,796,800,000 | [] | 124,838 |
61,658 | https://paperswithcode.com/paper/time-discounting-convolution-for-event | 1812.02395 | Time-Discounting Convolution for Event Sequences with Ambiguous Timestamps | This paper proposes a method for modeling event sequences with ambiguous
timestamps, a time-discounting convolution. Unlike in ordinary time series,
time intervals are not constant, small time-shifts have no significant effect,
and inputting timestamps or time durations into a model is not effective. The
criteria that ... | http://arxiv.org/abs/1812.02395v1 | http://arxiv.org/pdf/1812.02395v1.pdf | null | [
"Takayuki Katsuki",
"Takayuki Osogami",
"Akira Koseki",
"Masaki Ono",
"Michiharu Kudo",
"Masaki Makino",
"Atsushi Suzuki"
] | [
"Time Series"
] | 1,544,054,400,000 | [] | 61,722 |
219,761 | https://paperswithcode.com/paper/multimodal-deep-learning-framework-for-image | 2105.08809 | Multimodal Deep Learning Framework for Image Popularity Prediction on Social Media | Billions of photos are uploaded to the web daily through various types of social networks. Some of these images receive millions of views and become popular, whereas others remain completely unnoticed. This raises the problem of predicting image popularity on social media. The popularity of an image can be affected by ... | https://arxiv.org/abs/2105.08809v1 | https://arxiv.org/pdf/2105.08809v1.pdf | null | [
"Fatma S. Abousaleh",
"Wen-Huang Cheng",
"Neng-Hao Yu",
"Yu Tsao"
] | [
"Image popularity prediction",
"Multimodal Deep Learning"
] | 1,621,296,000,000 | [] | 187,072 |
129,836 | https://paperswithcode.com/paper/missing-class-robust-domain-adaptation-by | 2001.02015 | Missing-Class-Robust Domain Adaptation by Unilateral Alignment for Fault Diagnosis | Domain adaptation aims at improving model performance by leveraging the learned knowledge in the source domain and transferring it to the target domain. Recently, domain adversarial methods have been particularly successful in alleviating the distribution shift between the source and the target domains. However, these ... | https://arxiv.org/abs/2001.02015v1 | https://arxiv.org/pdf/2001.02015v1.pdf | null | [
"Qin Wang",
"Gabriel Michau",
"Olga Fink"
] | [
"Domain Adaptation"
] | 1,578,355,200,000 | [] | 90,096 |
289,794 | https://paperswithcode.com/paper/video-moment-retrieval-from-text-queries-via | 2204.09409 | Video Moment Retrieval from Text Queries via Single Frame Annotation | Video moment retrieval aims at finding the start and end timestamps of a moment (part of a video) described by a given natural language query. Fully supervised methods need complete temporal boundary annotations to achieve promising results, which is costly since the annotator needs to watch the whole moment. Weakly su... | https://arxiv.org/abs/2204.09409v3 | https://arxiv.org/pdf/2204.09409v3.pdf | null | [
"Ran Cui",
"Tianwen Qian",
"Pai Peng",
"Elena Daskalaki",
"Jingjing Chen",
"Xiaowei Guo",
"Huyang Sun",
"Yu-Gang Jiang"
] | [
"Contrastive Learning",
"Moment Retrieval"
] | 1,650,412,800,000 | [] | 178,188 |
271,256 | https://paperswithcode.com/paper/3d-face-morphing-attacks-generation | 2201.03454 | 3D Face Morphing Attacks: Generation, Vulnerability and Detection | Face Recognition systems (FRS) have been found vulnerable to morphing attacks, where the morphed face image is generated by blending the face images from contributory data subjects. This work presents a novel direction towards generating face morphing attacks in 3D. To this extent, we have introduced a novel approach b... | https://arxiv.org/abs/2201.03454v2 | https://arxiv.org/pdf/2201.03454v2.pdf | null | [
"Jag Mohan Singh",
"Raghavendra Ramachandra"
] | [
"Face Recognition"
] | 1,641,772,800,000 | [] | 156,979 |
165,222 | https://paperswithcode.com/paper/bandit-change-point-detection-for-real-time | 2009.11891 | Bandit Change-Point Detection for Real-Time Monitoring High-Dimensional Data Under Sampling Control | In many real-world problems of real-time monitoring high-dimensional streaming data, one wants to detect an undesired event or change quickly once it occurs, but under the sampling control constraint in the sense that one might be able to only observe or use selected components data for decision-making per time step in... | https://arxiv.org/abs/2009.11891v2 | https://arxiv.org/pdf/2009.11891v2.pdf | null | [
"Wanrong Zhang",
"Yajun Mei"
] | [
"Change Point Detection"
] | 1,600,905,600,000 | [] | 25,639 |
169,768 | https://paperswithcode.com/paper/matching-space-stereo-networks-for-cross | 2010.07347 | Matching-space Stereo Networks for Cross-domain Generalization | End-to-end deep networks represent the state of the art for stereo matching. While excelling on images framing environments similar to the training set, major drops in accuracy occur in unseen domains (e.g., when moving from synthetic to real scenes). In this paper we introduce a novel family of architectures, namely M... | https://arxiv.org/abs/2010.07347v1 | https://arxiv.org/pdf/2010.07347v1.pdf | null | [
"Changjiang Cai",
"Matteo Poggi",
"Stefano Mattoccia",
"Philippos Mordohai"
] | [
"Domain Generalization",
"Stereo Matching"
] | 1,602,633,600,000 | [] | 95,907 |
28,858 | https://paperswithcode.com/paper/learning-to-reason-with-adaptive-computation | 1610.07647 | Learning to Reason With Adaptive Computation | Multi-hop inference is necessary for machine learning systems to successfully
solve tasks such as Recognising Textual Entailment and Machine Reading. In this
work, we demonstrate the effectiveness of adaptive computation for learning the
number of inference steps required for examples of different complexity and
that l... | http://arxiv.org/abs/1610.07647v2 | http://arxiv.org/pdf/1610.07647v2.pdf | null | [
"Mark Neumann",
"Pontus Stenetorp",
"Sebastian Riedel"
] | [
"Natural Language Inference",
"Reading Comprehension"
] | 1,477,267,200,000 | [] | 92,047 |
169,009 | https://paperswithcode.com/paper/joint-semantic-analysis-with-document-level | 2010.05567 | Joint Semantic Analysis with Document-Level Cross-Task Coherence Rewards | Coreference resolution and semantic role labeling are NLP tasks that capture different aspects of semantics, indicating respectively, which expressions refer to the same entity, and what semantic roles expressions serve in the sentence. However, they are often closely interdependent, and both generally necessitate natu... | https://arxiv.org/abs/2010.05567v1 | https://arxiv.org/pdf/2010.05567v1.pdf | null | [
"Rahul Aralikatte",
"Mostafa Abdou",
"Heather Lent",
"Daniel Hershcovich",
"Anders Søgaard"
] | [
"Coreference Resolution",
"Natural Language Understanding",
"Semantic Role Labeling"
] | 1,602,460,800,000 | [] | 18,129 |
50,797 | https://paperswithcode.com/paper/quit-when-you-can-efficient-evaluation-of | 1806.11202 | Quit When You Can: Efficient Evaluation of Ensembles with Ordering Optimization | Given a classifier ensemble and a set of examples to be classified, many
examples may be confidently and accurately classified after only a subset of
the base models in the ensemble are evaluated. This can reduce both mean
latency and CPU while maintaining the high accuracy of the original ensemble.
To achieve such gai... | http://arxiv.org/abs/1806.11202v1 | http://arxiv.org/pdf/1806.11202v1.pdf | null | [
"Serena Wang",
"Maya Gupta",
"Seungil You"
] | [
"Combinatorial Optimization"
] | 1,530,144,000,000 | [] | 68,600 |
25,989 | https://paperswithcode.com/paper/causal-regularization | 1702.02604 | Causal Regularization | In application domains such as healthcare, we want accurate predictive models
that are also causally interpretable. In pursuit of such models, we propose a
causal regularizer to steer predictive models towards causally-interpretable
solutions and theoretically study its properties. In a large-scale analysis of
Electron... | http://arxiv.org/abs/1702.02604v2 | http://arxiv.org/pdf/1702.02604v2.pdf | null | [
"Mohammad Taha Bahadori",
"Krzysztof Chalupka",
"Edward Choi",
"Robert Chen",
"Walter F. Stewart",
"Jimeng Sun"
] | [
"Representation Learning"
] | 1,486,512,000,000 | [] | 142,958 |
268,054 | https://paperswithcode.com/paper/n-cps-generalising-cross-pseudo-supervision | 2112.07528 | n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation | We present n-CPS - a generalisation of the recent state-of-the-art cross pseudo supervision (CPS) approach for the task of semi-supervised semantic segmentation. In n-CPS, there are n simultaneously trained subnetworks that learn from each other through one-hot encoding perturbation and consistency regularisation. We a... | https://arxiv.org/abs/2112.07528v4 | https://arxiv.org/pdf/2112.07528v4.pdf | null | [
"Dominik Filipiak",
"Piotr Tempczyk",
"Marek Cygan"
] | [
"Semantic Segmentation",
"Semi-Supervised Semantic Segmentation"
] | 1,639,440,000,000 | [
{
"code_snippet_url": null,
"description": "**CutMix** is an image data augmentation strategy. Instead of simply removing pixels as in [Cutout](https://paperswithcode.com/method/cutout), we replace the removed regions with a patch from another image. The ground truth labels are also mixed proportionally to ... | 95,449 |
55,473 | https://paperswithcode.com/paper/exploring-the-vulnerability-of-single-shot | 1809.05966 | Exploring the Vulnerability of Single Shot Module in Object Detectors via Imperceptible Background Patches | Recent works succeeded to generate adversarial perturbations on the entire image or the object of interests to corrupt CNN based object detectors. In this paper, we focus on exploring the vulnerability of the Single Shot Module (SSM) commonly used in recent object detectors, by adding small perturbations to patches in ... | https://arxiv.org/abs/1809.05966v3 | https://arxiv.org/pdf/1809.05966v3.pdf | null | [
"Yuezun Li",
"Xiao Bian",
"Ming-Ching Chang",
"Siwei Lyu"
] | [
"Region Proposal"
] | 1,537,056,000,000 | [] | 60,178 |
142,195 | https://paperswithcode.com/paper/r-3-reverse-retrieve-and-rank-for-sarcasm | 2004.13248 | $R^3$: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge | We propose an unsupervised approach for sarcasm generation based on a non-sarcastic input sentence. Our method employs a retrieve-and-edit framework to instantiate two major characteristics of sarcasm: reversal of valence and semantic incongruity with the context which could include shared commonsense or world knowledg... | https://arxiv.org/abs/2004.13248v4 | https://arxiv.org/pdf/2004.13248v4.pdf | null | [
"Tuhin Chakrabarty",
"Debanjan Ghosh",
"Smaranda Muresan",
"Nanyun Peng"
] | [
"Scene Text Detection"
] | 1,588,032,000,000 | [
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L551",
"description": "A **Gated Linear Unit**, or **GLU** computes:\r\n\r\n$$ \\text{GLU}\\left(a, b\\right) = a\\otimes \\sigma\\left(b\\right) $$\r\n\r\nIt is used in nat... | 179,044 |
263,248 | https://paperswithcode.com/paper/learning-background-invariance-improves | null | Learning Background Invariance Improves Generalization and Robustness in Self-Supervised Learning on ImageNet and Beyond | Recent progress in self-supervised learning has demonstrated promising results in multiple visual tasks. An important ingredient in high-performing self-supervised methods is the use of data augmentation by training models to place different augmented views of the same image nearby in embedding space. However, commonly... | https://openreview.net/forum?id=zZnOG9ehfoO | https://openreview.net/pdf?id=zZnOG9ehfoO | NeurIPS Workshop ImageNet_PPF 2021 12 | [
"Chaitanya Ryali",
"David J. Schwab",
"Ari S. Morcos"
] | [
"Data Augmentation",
"Saliency Detection",
"Self-Supervised Learning",
"Unsupervised Saliency Detection"
] | 1,632,787,200,000 | [
{
"code_snippet_url": "",
"description": "BYOL (Bootstrap Your Own Latent) is a new approach to self-supervised learning. BYOL’s goal is to learn a representation $y_θ$ which can then be used for downstream tasks. BYOL uses two neural networks to learn: the online and target networks. The online network is ... | 80,178 |
36,585 | https://paperswithcode.com/paper/dynamic-concept-composition-for-zero-example | 1601.03679 | Dynamic Concept Composition for Zero-Example Event Detection | In this paper, we focus on automatically detecting events in unconstrained
videos without the use of any visual training exemplars. In principle,
zero-shot learning makes it possible to train an event detection model based on
the assumption that events (e.g. \emph{birthday party}) can be described by
multiple mid-level... | http://arxiv.org/abs/1601.03679v1 | http://arxiv.org/pdf/1601.03679v1.pdf | null | [
"Xiaojun Chang",
"Yi Yang",
"Guodong Long",
"Chengqi Zhang",
"Alexander G. Hauptmann"
] | [
"Event Detection",
"Zero-Shot Learning"
] | 1,452,729,600,000 | [] | 65,595 |
229,798 | https://paperswithcode.com/paper/power-law-graph-transformer-for-machine | 2107.02039 | Power Law Graph Transformer for Machine Translation and Representation Learning | We present the Power Law Graph Transformer, a transformer model with well defined deductive and inductive tasks for prediction and representation learning. The deductive task learns the dataset level (global) and instance level (local) graph structures in terms of learnable power law distribution parameters. The induct... | https://arxiv.org/abs/2107.02039v1 | https://arxiv.org/pdf/2107.02039v1.pdf | null | [
"Burc Gokden"
] | [
"Machine Translation",
"Quantization",
"Representation Learning"
] | 1,624,752,000,000 | [
{
"code_snippet_url": "",
"description": "**Absolute Position Encodings** are a type of position embeddings for [[Transformer](https://paperswithcode.com/method/transformer)-based models] where positional encodings are added to the input embeddings at the bottoms of the encoder and decoder stacks. The posit... | 61,409 |
308,770 | https://paperswithcode.com/paper/few-shot-class-incremental-learning-via-1 | 2207.11213 | Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay | Few-shot class-incremental learning (FSCIL) has been proposed aiming to enable a deep learning system to incrementally learn new classes with limited data. Recently, a pioneer claims that the commonly used replay-based method in class-incremental learning (CIL) is ineffective and thus not preferred for FSCIL. This has,... | https://arxiv.org/abs/2207.11213v1 | https://arxiv.org/pdf/2207.11213v1.pdf | null | [
"Huan Liu",
"Li Gu",
"Zhixiang Chi",
"Yang Wang",
"Yuanhao Yu",
"Jun Chen",
"Jin Tang"
] | [
"class-incremental learning",
"Incremental Learning",
"Knowledge Distillation"
] | 1,658,448,000,000 | [
{
"code_snippet_url": null,
"description": "A very simple way to improve the performance of almost any machine learning algorithm is to train many different models on the same data and then to average their predictions. Unfortunately, making predictions using a whole ensemble of models is cumbersome and may... | 148,573 |
50,897 | https://paperswithcode.com/paper/self-supervised-sparse-to-dense-self | 1807.00275 | Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera | Depth completion, the technique of estimating a dense depth image from sparse
depth measurements, has a variety of applications in robotics and autonomous
driving. However, depth completion faces 3 main challenges: the irregularly
spaced pattern in the sparse depth input, the difficulty in handling multiple
sensor moda... | http://arxiv.org/abs/1807.00275v2 | http://arxiv.org/pdf/1807.00275v2.pdf | null | [
"Fangchang Ma",
"Guilherme Venturelli Cavalheiro",
"Sertac Karaman"
] | [
"Autonomous Driving",
"Depth Completion"
] | 1,530,403,200,000 | [] | 11,907 |
226,924 | https://paperswithcode.com/paper/informative-class-activation-maps | 2106.10472 | Informative Class Activation Maps | We study how to evaluate the quantitative information content of a region within an image for a particular label. To this end, we bridge class activation maps with information theory. We develop an informative class activation map (infoCAM). Given a classification task, infoCAM depict how to accumulate information of p... | https://arxiv.org/abs/2106.10472v2 | https://arxiv.org/pdf/2106.10472v2.pdf | null | [
"Zhenyue Qin",
"Dongwoo Kim",
"Tom Gedeon"
] | [
"Classification",
"Image Classification"
] | 1,624,060,800,000 | [] | 153,132 |
307,845 | https://paperswithcode.com/paper/an-information-theoretic-analysis-of-bayesian | 2207.08735 | An Information-Theoretic Analysis of Bayesian Reinforcement Learning | Building on the framework introduced by Xu and Raginksy [1] for supervised learning problems, we study the best achievable performance for model-based Bayesian reinforcement learning problems. With this purpose, we define minimum Bayesian regret (MBR) as the difference between the maximum expected cumulative reward obt... | https://arxiv.org/abs/2207.08735v1 | https://arxiv.org/pdf/2207.08735v1.pdf | null | [
"Amaury Gouverneur",
"Borja Rodríguez-Gálvez",
"Tobias J. Oechtering",
"Mikael Skoglund"
] | [
"reinforcement-learning"
] | 1,658,102,400,000 | [] | 150,540 |
137,329 | https://paperswithcode.com/paper/asr-error-correction-and-domain-adaptation | 2003.07692 | ASR Error Correction and Domain Adaptation Using Machine Translation | Off-the-shelf pre-trained Automatic Speech Recognition (ASR) systems are an increasingly viable service for companies of any size building speech-based products. While these ASR systems are trained on large amounts of data, domain mismatch is still an issue for many such parties that want to use this service as-is lead... | https://arxiv.org/abs/2003.07692v1 | https://arxiv.org/pdf/2003.07692v1.pdf | null | [
"Anirudh Mani",
"Shruti Palaskar",
"Nimshi Venkat Meripo",
"Sandeep Konam",
"Florian Metze"
] | [
"Automatic Speech Recognition",
"Domain Adaptation",
"Machine Translation",
"Speaker Diarization",
"Speaker Diarization",
"Speech Recognition",
"Speech Recognition"
] | 1,584,057,600,000 | [] | 159,658 |
38,864 | https://paperswithcode.com/paper/sampled-weighted-min-hashing-for-large-scale | 1509.01771 | Sampled Weighted Min-Hashing for Large-Scale Topic Mining | We present Sampled Weighted Min-Hashing (SWMH), a randomized approach to
automatically mine topics from large-scale corpora. SWMH generates multiple
random partitions of the corpus vocabulary based on term co-occurrence and
agglomerates highly overlapping inter-partition cells to produce the mined
topics. While other a... | http://arxiv.org/abs/1509.01771v2 | http://arxiv.org/pdf/1509.01771v2.pdf | null | [
"Gibran Fuentes-Pineda",
"Ivan Vladimir Meza-Ruiz"
] | [
"Classification"
] | 1,441,497,600,000 | [
{
"code_snippet_url": null,
"description": "**Linear discriminant analysis** (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition, and machine learning to find a linear combination o... | 11,428 |
125,546 | https://paperswithcode.com/paper/neural-machine-translation-with-explicit | 1911.11520 | Neural Machine Translation with Explicit Phrase Alignment | While neural machine translation (NMT) has achieved state-of-the-art translation performance, it is unable to capture the alignment between the input and output during the translation process. The lack of alignment in NMT models leads to three problems: it is hard to (1) interpret the translation process, (2) impose le... | https://arxiv.org/abs/1911.11520v3 | https://arxiv.org/pdf/1911.11520v3.pdf | null | [
"Jiacheng Zhang",
"Huanbo Luan",
"Maosong Sun",
"FeiFei Zhai",
"Jingfang Xu",
"Yang Liu"
] | [
"Machine Translation"
] | 1,574,726,400,000 | [] | 186,834 |
221,193 | https://paperswithcode.com/paper/sample-efficient-reinforcement-learning-for | 2105.14016 | Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model | The curse of dimensionality is a widely known issue in reinforcement learning (RL). In the tabular setting where the state space $\mathcal{S}$ and the action space $\mathcal{A}$ are both finite, to obtain a nearly optimal policy with sampling access to a generative model, the minimax optimal sample complexity scales li... | https://arxiv.org/abs/2105.14016v2 | https://arxiv.org/pdf/2105.14016v2.pdf | NeurIPS 2021 12 | [
"Bingyan Wang",
"Yuling Yan",
"Jianqing Fan"
] | [
"Q-Learning",
"reinforcement-learning"
] | 1,622,160,000,000 | [
{
"code_snippet_url": null,
"description": "**Q-Learning** is an off-policy temporal difference control algorithm:\r\n\r\n$$Q\\left(S\\_{t}, A\\_{t}\\right) \\leftarrow Q\\left(S\\_{t}, A\\_{t}\\right) + \\alpha\\left[R_{t+1} + \\gamma\\max\\_{a}Q\\left(S\\_{t+1}, a\\right) - Q\\left(S\\_{t}, A\\_{t}\\right... | 187,376 |
65,464 | https://paperswithcode.com/paper/stanfords-graph-based-neural-dependency | null | Stanford's Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task | This paper describes the neural dependency parser submitted by Stanford to the CoNLL 2017 Shared Task on parsing Universal Dependencies. Our system uses relatively simple LSTM networks to produce part of speech tags and labeled dependency parses from segmented and tokenized sequences of words. In order to address the r... | https://aclanthology.org/K17-3002 | https://aclanthology.org/K17-3002.pdf | CONLL 2017 8 | [
"Timothy Dozat",
"Peng Qi",
"Christopher D. Manning"
] | [
"Dependency Parsing"
] | 1,501,545,600,000 | [
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L277",
"description": "**Sigmoid Activations** are a type of activation function for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{1}{\\left(1+\\exp\\left(-x\\right)\... | 52,190 |
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