{"list":"other","total":55,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"1910.10683","title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","created":"2019-10-23","cited_by_count":14599},{"id":"1907.11692","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","created":"2019-07-26","cited_by_count":13372},{"id":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"2001.08361","title":"Scaling Laws for Neural Language Models","created":"2020-01-23","cited_by_count":6586},{"id":"1804.07461","title":"GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding","created":"2018-04-20","cited_by_count":4899},{"id":"1606.05250","title":"SQuAD: 100,000+ Questions for Machine Comprehension of Text","created":"2016-06-16","cited_by_count":4148},{"id":"2004.04906","title":"Dense Passage Retrieval for Open-Domain Question Answering","created":"2020-04-10","cited_by_count":3824},{"id":"1809.09600","title":"HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering","created":"2018-09-25","cited_by_count":3754},{"id":"1606.08415","title":"Gaussian Error Linear Units (GELUs)","created":"2016-06-27","cited_by_count":3502},{"id":"2004.05150","title":"Longformer: The Long-Document Transformer","created":"2020-04-10","cited_by_count":3413},{"id":"1909.11942","title":"ALBERT: A Lite BERT for Self-supervised Learning of Language Representations","created":"2019-09-26","cited_by_count":2790},{"id":"1704.05426","title":"A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference","created":"2017-04-18","cited_by_count":2682},{"id":"2104.08821","title":"SimCSE: Simple Contrastive Learning of Sentence Embeddings","created":"2021-04-18","cited_by_count":2173},{"id":"1506.02626","title":"Learning both Weights and Connections for Efficient Neural Networks","created":"2015-06-08","cited_by_count":2031},{"id":"1806.03822","title":"Know What You Don't Know: Unanswerable Questions for SQuAD","created":"2018-06-11","cited_by_count":1554},{"id":"2005.10242","title":"Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere","created":"2020-05-20","cited_by_count":1405},{"id":"2112.04426","title":"Improving language models by retrieving from trillions of tokens","created":"2021-12-08","cited_by_count":1293},{"id":"1708.00055","title":"SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation","created":"2017-07-31","cited_by_count":1001},{"id":"1805.12471","title":"Neural Network Acceptability Judgments","created":"2018-05-31","cited_by_count":958},{"id":"2208.03299","title":"Atlas: Few-shot Learning with Retrieval Augmented Language Models","created":"2022-08-05","cited_by_count":952},{"id":"2007.00808","title":"Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval","created":"2020-07-01","cited_by_count":849},{"id":"1708.06519","title":"Learning Efficient Convolutional Networks through Network Slimming","created":"2017-08-22","cited_by_count":676},{"id":"2003.03033","title":"What is the State of Neural Network Pruning?","created":"2020-03-06","cited_by_count":583},{"id":"1506.02557","title":"Variational Dropout and the Local Reparameterization Trick","created":"2015-06-08","cited_by_count":490},{"id":"2010.10144","title":"Keystroke Dynamics as Part of Lifelogging","created":"2020-10-20","cited_by_count":334},{"id":"2003.07820","title":"Overview of the TREC 2019 deep learning track","created":"2020-03-17","cited_by_count":286},{"id":"2106.14448","title":"R-Drop: Regularized Dropout for Neural Networks","created":"2021-06-28","cited_by_count":202},{"id":"2102.07662","title":"Overview of the TREC 2020 deep learning track","created":"2021-02-15","cited_by_count":196},{"id":"1710.10723","title":"Simple and Effective Multi-Paragraph Reading Comprehension","created":"2017-10-29","cited_by_count":120},{"id":"2109.04448","title":"Vision-and-Language or Vision-for-Language? On Cross-Modal Influence in Multimodal Transformers","created":"2021-09-09","cited_by_count":72},{"id":"2106.14807","title":"A Few Brief Notes on DeepImpact, COIL, and a Conceptual Framework for Information Retrieval Techniques","created":"2021-06-28","cited_by_count":69},{"id":"2203.08773","title":"Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data","created":"2022-03-16","cited_by_count":61},{"id":"1901.08644","title":"Ablation Studies in Artificial Neural Networks","created":"2019-01-24","cited_by_count":60},{"id":"1602.06359","title":"Text Matching as Image Recognition","created":"2016-02-20","cited_by_count":59},{"id":"1911.00484","title":"Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents","created":"2019-11-01","cited_by_count":57},{"id":"1906.03728","title":"The Generalization-Stability Tradeoff In Neural Network Pruning","created":"2019-06-09","cited_by_count":54},{"id":"1912.05891","title":"SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval","created":"2019-12-12","cited_by_count":48},{"id":"2108.13454","title":"Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback","created":"2021-08-30","cited_by_count":47},{"id":"1904.13310","title":"Survey of Dropout Methods for Deep Neural Networks","created":"2019-04-25","cited_by_count":44},{"id":"2009.07258","title":"BERT-QE: Contextualized Query Expansion for Document Re-ranking","created":"2020-09-15","cited_by_count":40},{"id":"2105.06020","title":"Are Larger Pretrained Language Models Uniformly Better? Comparing Performance at the Instance Level","created":"2021-05-13","cited_by_count":34},{"id":"2109.06747","title":"Adaptive Information Seeking for Open-Domain Question Answering","created":"2021-09-14","cited_by_count":29},{"id":"1805.08092","title":"Efficient and Robust Question Answering from Minimal Context over Documents","created":"2018-05-21","cited_by_count":26},{"id":"2105.07975","title":"How Deep is your Learning: the DL-HARD Annotated Deep Learning Dataset","created":"2021-05-17","cited_by_count":18},{"id":"1711.00066","title":"Fraternal Dropout","created":"2017-10-31","cited_by_count":14},{"id":"2204.02725","title":"Match-Prompt: Improving Multi-task Generalization Ability for Neural Text Matching via Prompt Learning","created":"2022-04-06","cited_by_count":10},{"id":"1609.08017","title":"Dropout with Expectation-linear Regularization","created":"2016-09-26","cited_by_count":9},{"id":"1901.03866","title":"HAS-QA: Hierarchical Answer Spans Model for Open-domain Question Answering","created":"2019-01-12","cited_by_count":8},{"id":"2005.11223","title":"L2R2: Leveraging Ranking for Abductive Reasoning","created":"2020-05-22","cited_by_count":8},{"id":"2101.06423","title":"Match-Ignition: Plugging PageRank into Transformer for Long-form Text Matching","created":"2021-01-16","cited_by_count":5},{"id":"2104.08744","title":"Generative Context Pair Selection for Multi-hop Question Answering","created":"2021-04-18","cited_by_count":2},{"id":"2204.11545","title":"LoL: A Comparative Regularization Loss over Query Reformulation Losses for Pseudo-Relevance Feedback","created":"2022-04-25","cited_by_count":1}],"next_cursor":null}