Neural Network
Benchmark
Word Embedding
Security
Explainable Artificial Intelligence (XAI)
Language Model
Computer Vision
Reinforcement Learning
Information Retrieval
Tabular Learning
Meta Learning
Continual Learning
Mixture of Experts
Model Compression
Knoweldge Distillation
Quantization
Theme | Number | Title | Journal/Conference | Date | Author | Link |
---|---|---|---|---|---|---|
Neural Network |
1 | Random Forests | Machine Learning, Volume 45 | 2001-01-01 | Leo Breiman | Link |
2 | Visualizing Data using t-SNE | ICLR 2015 | 2008-01-01 | Laurens van der Maaten et al | Link | |
3 | Dropout: A Simple Way to Prevent Neural Networks from Overfitting | JMLR 2014 | 2014-01-01 | Nitish Srivastava et al | Link | |
4 | Adam: A Method for Stochastic Optimization | ACL 2015 | 2014-12-22 | Diederik P. Kingma et al | Link | |
5 | Convolutional Neural Networks for Sentence Classification | EMNLP 2014 | 2014-08-25 | Yoon Kim et al | Link | |
6 | Efficient Per-Example Gradient Computations | ICML 2016 | 2015-10-07 | Ian Goodfellow et al | Link | |
7 | XGBoost: A Scalable Tree Boosting System | ACM CCS 2016 | 2016-03-09 | Tianqi Chen et al | Link | |
8 | Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation | 2016-07-01 | Dong Yu et al | Link | ||
9 | SGDR: Stochastic Gradient Descent with Warm Restarts | IEEE 2017 | 2016-08-13 | Ilya Loshchilov et al | Link | |
10 | Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour | NeurIPS 2017 | 2017-06-08 | Priya Goyal et al | Link | |
11 | Don't Decay the Learning Rate, Increase the Batch Size | ICLR 2018 | 2017-11-01 | Samuel L. Smith et al | Link | |
12 | Population Based Training of Neural Networks | ACL 2018 | 2017-11-27 | Samuel L. Smith et al | Link | |
13 | A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay | PBML | 2018-03-26 | Leslie N. Smith et al | Link | |
14 | Dying ReLU and Initialization: Theory and Numerical Examples | 2019-03-15 | Lu Lu et al | Link | ||
15 | Permute, Quantize, and Fine-tune: Efficient Compression of Neural Networks | PMLR 2021 | 2020-10-29 | Julieta Martinez et al | Link | |
16 | Ensemble deep learning: A review | 2021-04-06 | M. A. Ganaie et al | Link | ||
17 | R-Drop: Regularized Dropout for Neural Networks | ICML 2016 | 2021-06-28 | Xiaobo Liang et al | Link | |
Benchmark | 18 | BLEU: a method for automatic evaluation of machine translation | ACL 2002 | 2002-07-01 | Kishore Papineni et al | Link |
19 | METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments | ACL 2005 | 2005-01-01 | Satanjeev Banerjee et al | Link | |
20 | GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding | 2018-04-20 | Alex Wang et al | Link | ||
21 | A Call for Clarity in Reporting BLEU Scores | ACL 2018 | 2018-04-23 | Matt Post et al | Link | |
22 | BERTScore: Evaluating Text Generation with BERT | ACL 2020 | 2019-04-21 | Tianyi Zhang et al | Link | |
23 | BLEURT: Learning Robust Metrics for Text Generation | ACL 2020 | 2020-04-09 | Thibault Sellam et al | Link | |
24 | KLUE: Korean Language Understanding Evaluation | 2021-05-20 | Sungjoon Park et al | Link | ||
25 | ROUGE: A Package for Automatic Evaluation of Summaries | Artificial Intelligence Volume 299 | 2021-10-01 | Chin-Yew Lin et al | Link | |
Word Embedding | 26 | Efficient Estimation of Word Representations in Vector Space | 2013-01-16 | Tomas Mikolov et al | Link | |
27 | Linguistic Regularities in Continuous Space Word Representations | 2013-06-01 | Tomas Mikolov et al | Link | ||
28 | Dear Sir or Madam, May I introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer | 2018-03-17 | Sudha Rao et al | Link | ||
29 | SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing | ACL 2019 | 2018-08-19 | Taku Kudo et al | Link | |
30 | Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks | AAAI 2020 | 2019-08-27 | Nils Reimers et al | Link | |
31 | SimCSE: Simple Contrastive Learning of Sentence Embeddings | EMNLP 2021 | 2021-04-18 | Tianyu Gao et al | Link | |
32 | Self-Guided Contrastive Learning for BERT Sentence Representations | ACL 2021 | 2021-06-03 | Taeuk Kim et al | Link | |
33 | Contrastive Learning of Sentence Embeddings from Scratch | 2023-05-24 | Junlei Zhang et al | Link | ||
Explainable Artificial Intelligence (XAI) | 34 | Building Machines That Learn and Think Like People | NeurIPS 2016 | 2016-04-01 | Brenden M. Lake et al | Link |
35 | A Multiscale Visualization of Attention in the Transformer Model | ACL 2019 | 2019-06-12 | Jesse Vig et al | Link | |
36 | On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines | IEEE Open Journal of the Computer Society | 2020-06-08 | Marius Mosbach et al | Link | |
37 | Reliable Post hoc Explanations: Modeling Uncertainty in Explainability | 2020-08-11 | Dylan Slack et al | Link | ||
38 | Reward is enough | ICLR 2022 | 2021-10-01 | David Silver et al | Link | |
Language Model | 39 | Large-Scale Distributed Language Modeling | IEEE 2007 | 2007-04-05 | Ahmad Emami et al | Link |
40 | Large Language Models in Machine Translation | Teaching and Learning in Higher Education | 2007-06-01 | Gloria Brown Wright et al | Link | |
41 | Neural Machine Translation by Jointly Learning to Align and Translate | NeurIPS 2014 | 2014-09-01 | Dzmitry Bahdanau et al | Link | |
42 | Sequence to Sequence Learning with Neural Networks | ICML 2015 | 2014-09-10 | Ilya Sutskever et al | Link | |
43 | Neural Machine Translation of Rare Words with Subword Units | ICLR 2016 | 2015-08-31 | Rico Sennrich et al | Link | |
44 | Continuous control with deep reinforcement learning | 2015-09-09 | Timothy P. Lillicrap et al | Link | ||
45 | Unsupervised Deep Embedding for Clustering Analysis | 2015-11-19 | Junyuan Xie et al | Link | ||
46 | Hierarchical Attention Networks for Document Classification | KDD 2016 | 2016-01-01 | Zichao Yang et al | Link | |
47 | Hierarchical Attention Networks for Document Classification | 2016-01-01 | Zichao Yang et al | Link | ||
48 | Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation | 2016-09-26 | Yonghui Wu et al | Link | ||
49 | Understanding deep learning requires rethinking generalization | ICLR 2017 | 2016-11-10 | Chiyuan Zhang et al | Link | |
50 | SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents | AAAI 2017 | 2016-11-14 | Ramesh Nallapati et al | Link | |
51 | Reading Wikipedia to Answer Open-Domain Questions | ACL 2017 | 2017-03-31 | Danqi Chen et al | Link | |
52 | Get To The Point: Summarization with Pointer-Generator Networks | 2017-04-14 | Abigail See et al | Link | ||
53 | Learning to Ask: Neural Question Generation for Reading Comprehension | ACL 2017 | 2017-04-29 | Xinya Du et al | Link | |
54 | Style Transfer from Non-Parallel Text by Cross-Alignment | 2017-05-26 | Tianxiao Shen et al | Link | ||
55 | Attention Is All You Need | 2017-06-12 | Ashish Vaswani et al | Link | ||
56 | Adversarial Examples for Evaluating Reading Comprehension Systems | 2017-07-23 | Robin Jia et al | Link | ||
57 | Self-Attention with Relative Position Representations too? | NAACL 2018 | 2018-01-01 | Peter Shaw et al | Link | |
58 | Personalizing Dialogue Agents: I have a dog, do you have pets too? | WMT 2018 | 2018-01-22 | Saizheng Zhang et al | Link | |
59 | Deep contextualized word representations | 2018-02-15 | Matthew E. Peters et al | Link | ||
60 | Training Tips for the Transformer Model | 2018-04-01 | Martin Popel et al | Link | ||
61 | Hierarchical Neural Story Generation | 2018-05-13 | Angela Fan et al | Link | ||
62 | Know What You Don't Know: Unanswerable Questions for SQuAD | ACL 2018 | 2018-06-11 | Pranav Rajpurkar et al | Link | |
63 | BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding | 2018-10-11 | Jacob Devlin et al | Link | ||
64 | A Recurrent BERT-based Model for Question Generation | ACL 2019 | 2019-01-01 | Ying-Hong Chan et al | Link | |
65 | EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks | 2019-01-31 | Jason Wei et al | Link | ||
66 | Good-Enough Compositional Data Augmentation | 2019-04-21 | Jacob Andreas et al | Link | ||
67 | Language Models are Unsupervised Multitask Learners | 2019-06-01 | Alec Radford et al | Link | ||
68 | SpanBERT: Improving Pre-training by Representing and Predicting Spans | 2019-07-24 | Mandar Joshi et al | Link | ||
69 | TabNet: Attentive Interpretable Tabular Learning | EMNLP 2019 | 2019-08-20 | Sercan O. Arik et al | Link | |
70 | Text Summarization with Pretrained Encoders | 2019-08-22 | Yang Liu et al | Link | ||
71 | Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks | 2019-08-27 | Nils Reimers et al | Link | ||
72 | Alpaca: Intermittent Execution without Checkpoints | NeurIPS 2019 | 2019-09-13 | Kiwan Maeng et al | Link | |
73 | Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer | ACL 2020 | 2019-10-23 | Colin Raffel et al | Link | |
74 | BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension | 2019-10-29 | Mike Lewis et al | Link | ||
75 | Fast Transformer Decoding: One Write-Head is All You Need | 2019-11-06 | Noam Shazeer et al | Link | ||
76 | Improving Transformer Optimization Through Better Initialization | NeurIPS 2020 | 2020-01-01 | Xiao Shi Huang et al | Link | |
77 | Data Augmentation using Pre-trained Transformer Models | AACL 2020 | 2020-03-04 | Varun Kumar et al | Link | |
78 | Dense Passage Retrieval for Open-Domain Question Answering | EMNLP 2020 | 2020-04-10 | Vladimir Karpukhin et al | Link | |
79 | Understanding the Difficulty of Training Transformers | 2020-04-17 | Liyuan Liu et al | Link | ||
80 | RoFormer: Enhanced Transformer with Rotary Position Embedding | 2021-04-20 | Jianlin Su et al | Link | ||
81 | Finetuned Language Models Are Zero-Shot Learners | IEEE/RJS International Conference on Intelligent RObots and Systems | 2021-09-03 | Jason Wei et al | Link | |
82 | Post-Training with Interrogative Sentences for Enhancing BART-based Korean Question Generator | ACL 2022 | 2022-01-01 | Gyu-Min Park et al | Link | |
83 | FNet: Mixing Tokens with Fourier Transforms | NAACL 2022 | 2022-01-01 | James Lee-Thorp et al | Link | |
84 | BERTopic: Neural topic modeling with a class-based TF-IDF procedure | 2022-03-11 | Maarten Grootendorst et al | Link | ||
85 | Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks | EMNLP 2022 | 2022-04-16 | Yizhong Wang et al | Link | |
86 | MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification | ACL 2020 | 2022-04-25 | Jiaao Chen et al | Link | |
87 | Learning to Transfer Prompts for Text Generation | NAACL 2022 | 2022-05-03 | Junyi Li et al | Link | |
88 | On the Use of BERT for Automated Essay Scoring: Joint Learning of Multi-Scale Essay Representation | EMNLP 2022 | 2022-05-08 | Yongjie Wang et al | Link | |
89 | KOLD: Korean Offensive Language Dataset | 2022-05-23 | Younghoon Jeong et al | Link | ||
90 | CoNT: Contrastive Neural Text Generation | 2022-05-29 | Chenxin An et al | Link | ||
91 | GODEL: Large-Scale Pre-Training for Goal-Directed Dialog | 2022-06-22 | Baolin Peng et al | Link | ||
92 | Generative Language Models for Paragraph-Level Question Generation | EMNLP 2022 | 2022-10-08 | Asahi Ushio et al | Link | |
93 | Self-Instruct: Aligning Language Models with Self-Generated Instructions | ACL 2023 | 2022-12-20 | Yizhong Wang et al | Link | |
94 | Dialog-Post Multi-Level Self-Supervised Objectives and Hierarchical Model for Dialogue Post-Training | ACL 2023 | 2023-01-01 | Zhenyu Zhang et al | Link | |
95 | LLaMA: Open and Efficient Foundation Language Models | 2023-02-27 | Hugo Touvron et al | Link | ||
96 | Instruction Tuning with GPT-4 | EMNLP 2023 | 2023-04-06 | Baolin Peng et al | Link | |
97 | Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Prompting | 2023-05-07 | Miles Turpin et al | Link | ||
98 | An Empirical Comparison of LM-based Question and Answer Generation Methods | ACL 2023 | 2023-05-26 | Asahi Ushio et al | Link | |
99 | A Practical Toolkit for Multilingual Question and Answer Generation | 2023-05-27 | Asahi Ushio et al | Link | ||
100 | Mistral 7B | 2023-10-10 | Albert Q. Jiang et al | Link | ||
101 | Gemma: Open Models Based on Gemini Research and Technology | 2024-03-13 | Gemma Team et al | Link | ||
Meta Learning | 102 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | ICML 2017 | 2017-03-09 | Chelsea Finn et al | Link |
103 | Optimization as A Model for Few-shot Learning | ICLR 2017 | 2017-07-22 | Sachin Ravi et al | Link | |
104 | BERT Learns to Teach: Knowledge Distillation with Meta Learning | 2021-06-08 | Wangchunshu Zhou et al | Link | ||
Continual Learning | 105 | Overcoming catastrophic forgetting in neural networks | 2016-12-02 | James Kirkpatrick et al | Link | |
Mixture of Experts | 106 | Adaptive Mixtures of Local Experts | MIT Press 1991 | 1991-03-01 | Robert A. Jacobs et al | Link |
Model Compression | 107 | Model Compression | ACM SIGKDD 2006 | 2006-08-20 | Cristian Bucil˘a et al | Link |
108 | Adaptive Computation Time for Recurrent Neural Networks | Behavioral and Brain Sciences | 2016-03-29 | Alex Graves et al | Link | |
109 | BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks | 2017-09-06 | Surat Teerapittayanon et al | Link | ||
110 | FastBERT: a Self-distilling BERT with Adaptive Inference Time | PMLR 2020 | 2020-04-05 | Weijie Liu et al | Link | |
111 | A Survey on Model Compression and Acceleration for Pretrained Language Models | ICLR 2022 | 2022-02-15 | Canwen Xu et al | Link | |
Knoweldge Distillation | 112 | Distilling the Knowledge in a Neural Network | NIPS 2014 | 2015-03-09 | Geoffrey Hinton et al | Link |
113 | Improved Knowledge Distillation via Teacher Assistant | ACSAC 2019 | 2019-02-09 | Seyed-Iman Mirzadeh et al | Link | |
114 | Unified Language Model Pre-training for Natural Language Understanding and Generation | 2019-05-08 | Li Dong et al | Link | ||
115 | Patient Knowledge Distillation for BERT Model Compression | EMNLP 2019 | 2019-08-25 | Siqi Sun et al | Link | |
116 | DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter | JMLR | 2019-10-02 | Victor Sanh et al | Link | |
117 | MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers | 2020-02-25 | Wenhui Wang et al | Link | ||
118 | MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers | PMLR 2021 | 2020-12-31 | Wenhui Wang et al | Link | |
119 | Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes | ACL 2023 | 2023-05-03 | Cheng-Yu Hsieh et al | Link | |
Quantization | 120 | Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation | NeurIPS 2013 | 2013-08-15 | Yoshua Bengio et al | Link |
121 | XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks | 2016-03-16 | Mohammad Rastegari et al | Link | ||
122 | DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients | IEEE 2017 | 2016-06-20 | Shuchang Zhou et al | Link | |
123 | Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations | 2016-09-22 | Itay Hubara et al | Link | ||
124 | Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference | 2017-12-15 | Benoit Jacob et al | Link | ||
125 | And the Bit Goes Down: Revisiting the Quantization of Neural Networks | TACL 2020 | 2019-07-12 | Pierre Stock et al | Link | |
126 | Learned Step Size Quantization | ICLR 2020 | 2019-02-21 | Steven K. Esser et al | Link | |
127 | Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT | 2019-09-12 | Sheng Shen et al | Link | ||
128 | Quantization Networks | 2019-11-21 | Jiwei Yang et al | Link | ||
129 | ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions | 2020-03-07 | Zechun Liu et al | Link | ||
130 | Binary Neural Networks: A Survey | 2020-03-31 | Haotong Qin et al | Link | ||
131 | Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation | ACSAC 2021 | 2020-04-20 | Hao Wu et al | Link | |
132 | BinaryBERT: Pushing the Limit of BERT Quantization | ACL-IJCNLP 2021 | 2020-12-31 | Haoli Bai et al | Link | |
133 | I-BERT: Integer-only BERT Quantization | USENIX 2021 | 2021-01-05 | Sehoon Kim et al | Link | |
134 | A Survey of Quantization Methods for Efficient Neural Network Inference | AAAI 2022 | 2021-03-25 | Amir Gholami et al | Link | |
135 | A White Paper on Neural Network Quantization | ICLR 2022 | 2021-06-15 | Markus Nagel et al | Link | |
136 | BiBERT: Accurate Fully Binarized BERT | 2022-03-12 | Haotong Qin et al | Link | ||
137 | BiT: Robustly Binarized Multi-distilled Transformer | IPS 2022 | 2022-05-25 | Zechun Liu et al | Link | |
138 | NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers | CVPR 2023 | 2022-11-29 | Yijiang Liu et al | Link | |
139 | QuIP: 2-Bit Quantization of Large Language Models With Guarantees | 2023-07-25 | Jerry Chee et al | Link | ||
Reinforcement Learning | 140 | Playing Atari with Deep Reinforcement Learning | ICLR 2014 | 2013-12-19 | Volodymyr Mnih et al | Link |
141 | Proximal Policy Optimization Algorithms | 2017-07-20 | John Schulman et al | Link | ||
142 | Rainbow: Combining Improvements in Deep Reinforcement Learning | AAAI 2018 | 2017-10-06 | Matteo Hessel et al | Link | |
143 | Time Limits in Reinforcement Learning | CVPR 2018 | 2017-12-01 | Fabio Pardo et al | Link | |
144 | Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research | NAACL 2018 | 2018-02-26 | Matthias Plappert et al | Link | |
145 | CURL: Contrastive Unsupervised Representations for Reinforcement Learning | 2020-04-08 | Aravind Srinivas et al | Link | ||
146 | Goal Density based Hindsight Experience Prioritization for Multi Goal Robot Manipulation Reinforcement Learning | ICLR 2021 | 2020-09-04 | Yingyi Kuang et al | Link | |
147 | The Role of Tactile Sensing in Learning and Deploying Grasp Refinement Algorithms | 2021-09-23 | Alexander Koenig et al | Link | ||
148 | The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks | 2021-10-12 | Rahim Entezari et al | Link | ||
149 | SoMoGym: A Toolkit for Developing and Evaluating Controllers and Reinforcement Learning Algorithms for Soft Robots | IEEE Robotics and Automation Letters 2022 | 2022-01-01 | Moritz A. Graule et al | Link | |
150 | Learning-Based Slip Detection for Robotic Fruit Grasping and Manipulation under Leaf Interference | NeurIPS 2022 | 2022-06-01 | Hongyu Zhou et al | Link | |
151 | Augmenting Vision-Based Grasp Plans for Soft Robotic Grippers using Reinforcement Learning | NeurIPS 2022 | 2022-08-24 | Vighnesh Vatsal et al | Link | |
152 | DreamWaQ: Learning Robust Quadrupedal Locomotion With Implicit Terrain Imagination via Deep Reinforcement Learning | ICRA 2023 | 2023-01-25 | I Made Aswin Nahrendra et al | Link | |
Information Retrieval | 153 | Document Expansion by Query Prediction | 2019-04-17 | Rodrigo Nogueira et al | Link | |
154 | Latent Retrieval for Weakly Supervised Open Domain Question Answering | 2019-06-01 | Kenton Lee et al | Link | ||
155 | ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT | SIGIR 2020 | 2020-04-27 | Omar Khattab et al | Link | |
156 | Differentially Private Learning Needs Better Features (or Much More Data) | 2020-11-23 | Florian Tramèr et al | Link | ||
157 | Learning Dense Representations of Phrases at Scale | ACL 2021 | 2020-12-23 | Jinhyuk Lee et al | Link | |
158 | BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models | 2021-04-17 | Nandan Thakur et al | Link | ||
159 | Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval | ICLR 2022 | 2021-08-12 | Luyu Gao et al | Link | |
160 | Multi-View Document Representation Learning for Open-Domain Dense Retrieval | ACL 2022 | 2022-03-16 | Shunyu Zhang et al | Link | |
161 | Learning Diverse Document Representations with Deep Query Interactions for Dense Retrieval | 2022-08-08 | Zehan Li et al | Link | ||
162 | CAPSTONE: Curriculum Sampling for Dense Retrieval with Document Expansion | EMNLP 2023 | 2022-12-18 | Xingwei He et al | Link | |
Tabular Learning | 163 | Compositional Semantic Parsing on Semi-Structured Tables | 2015-08-03 | Panupong Pasupat et al | Link | |
164 | The E2E Dataset: New Challenges For End-to-End Generation | 2017-06-28 | Jekaterina Novikova et al | Link | ||
165 | Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning | 2017-08-31 | Victor Zhong et al | Link | ||
166 | TAPAS: Weakly Supervised Table Parsing via Pre-training | ACL 2020 | 2020-04-05 | Jonathan Herzig et al | Link | |
167 | TabTransformer: Tabular Data Modeling Using Contextual Embeddings | USENIX 2021 | 2020-12-11 | Xin Huang et al | Link | |
168 | FeTaQA: Free-form Table Question Answering | 2020-04-01 | Linyong Nan et al | Link | ||
169 | DoT: An efficient Double Transformer for NLP tasks with tables | TACL 2022 | 2021-01-01 | Syrine Krichene et al | Link | |
170 | TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance | NeurIPS 2021 | 2021-05-17 | Fengbin Zhu et al | Link | |
171 | SpreadsheetCoder: Formula Prediction from Semi-structured Context | ICML 2021 | 2021-06-26 | Xinyun Chen et al | Link | |
172 | Multi-Row, Multi-Span Distant Supervision For Table+Text Question | 2021-12-14 | Vishwajeet Kumar et al | Link | ||
173 | OmniTab: Pretraining with Natural and Synthetic Data for Few-shot Table-based Question Answering | NeurIPS 2022 | 2022-07-08 | Zhengbao Jiang et al | Link | |
174 | Why do tree-based models still outperform deep learning on tabular data? | 2022-07-18 | Léo Grinsztajn et al | Link | ||
175 | Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning | SIGIR 2023 | 2023-01-31 | Yunhu Ye et al | Link | |
176 | DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction | IPS 2023 | 2023-04-21 | Mohammadreza Pourreza et al | Link | |
177 | CABINET: Content Relevance based Noise Reduction for Table Question Answering | ICLR 2024 | 2024-01-01 | Sohan Patnaik et al | Link | |
Security | 178 | Deep Learning with Differential Privacy | 2016-07-01 | Martín Abadi et al | Link | |
179 | Membership Inference Attacks against Machine Learning Models | 2016-10-18 | Reza Shokri et al | Link | ||
180 | Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks | IEEE Symposium on Security and Privacy (SP) 2019 | 2019-01-01 | Bolun Wang et al | Link | |
181 | STRIP: A Defence Against Trojan Attacks on Deep Neural Networks | ICLR 2020 | 2019-02-18 | Yansong Gao et al | Link | |
182 | BadNets: Evaluating Backdooring Attacks on Deep Neural Networks | NeurIPS 2019 | 2019-04-11 | Tianyu Gu et al | Link | |
183 | Regula Sub-rosa: Latent Backdoor Attacks on Deep Neural Networks | OpenAI | 2019-05-24 | Yuanshun Yao et al | Link | |
184 | Weight Poisoning Attacks on Pre-trained Models | ACL 2020 | 2020-04-14 | Keita Kurita et al | Link | |
185 | BadNL: Backdoor Attacks against NLP Models with Semantic-preserving Improvements | ACSAC 2021 | 2020-06-01 | Xiaoyi Chen et al | Link | |
186 | Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review | IEEE 2021 | 2020-07-21 | Yansong Gao et al | Link | |
187 | Trojaning Language Models for Fun and Profit | NeurIPS 2021 | 2020-08-01 | Xinyang Zhang et al | Link | |
188 | Scaling up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping | PETS 2021 | 2020-09-07 | Jaewoo Lee et al | Link | |
189 | TextHide: Tackling Data Privacy in Language Understanding Tasks | EMNLP 2020 | 2020-10-02 | Yangsibo Huang et al | Link | |
190 | Extracting Training Data from Large Language Models | 2020-12-14 | Nicholas Carlini et al | Link | ||
191 | Hidden Backdoors in Human-Centric Language Models | CCS 2021 | 2021-01-01 | Shaofeng Li et al | Link | |
192 | T-Miner: A Generative Approach to Defend Against Trojan Attacks on DNN-based Text Classification | 2021-03-07 | Ahmadreza Azizi et al | Link | ||
193 | Large Language Models Can Be Strong Differentially Private Learners | 2021-10-12 | Xuechen Li et al | Link | ||
194 | Hidden Trigger Backdoor Attack on NLP Models via Linguistic Style Manipulation | USENIX Security 2022 | 2022-01-01 | Xudong Pan et al | Link | |
Computer Vision | 195 | Auto-Encoding Variational Bayes | 2013-12-20 | Diederik P Kingma et al | Link | |
196 | Show, Attend and Tell: Neural Image Caption Generation with Visual Attention | 2015-02-10 | Kelvin Xu et al | Link | ||
197 | InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets | 2016-06-12 | Xi Chen et al | Link | ||
198 | The Perception-Distortion Tradeoff | CVPR 2018 | 2017-11-16 | Yochai Blau et al | Link | |
199 | Do CIFAR-10 Classifiers Generalize to CIFAR-10? | 2018-06-01 | Benjamin Recht et al | Link | ||
200 | Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks | ICRA 2019 | 2018-10-24 | Michelle A. Lee et al | Link | |
201 | An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale | CVPR 2021 | 2020-10-22 | Alexey Dosovitskiy et al | Link | |
202 | Training data-efficient image transformers & distillation through attention | ACL 2021 | 2020-12-23 | Hugo Touvron et al | Link |
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