{"list":"other","total":52,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"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":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"2203.02155","title":"Training language models to follow instructions with human feedback","created":"2022-03-04","cited_by_count":15971},{"id":"2302.13971","title":"LLaMA: Open and Efficient Foundation Language Models","created":"2023-02-27","cited_by_count":14132},{"id":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"1409.0575","title":"ImageNet Large Scale Visual Recognition Challenge","created":"2014-09-01","cited_by_count":8498},{"id":"1502.03167","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","created":"2015-02-11","cited_by_count":7005},{"id":"1607.06450","title":"Layer Normalization","created":"2016-07-21","cited_by_count":5669},{"id":"1409.0473","title":"Neural Machine Translation by Jointly Learning to Align and Translate","created":"2014-09-01","cited_by_count":4548},{"id":"2204.14198","title":"Flamingo: a Visual Language Model for Few-Shot Learning","created":"2022-04-29","cited_by_count":4386},{"id":"1411.7766","title":"Deep Learning Face Attributes in the Wild","created":"2014-11-28","cited_by_count":3693},{"id":"2203.15556","title":"Training Compute-Optimal Large Language Models","created":"2022-03-29","cited_by_count":3019},{"id":"1611.01144","title":"Categorical Reparameterization with Gumbel-Softmax","created":"2016-11-03","cited_by_count":2739},{"id":"1412.3555","title":"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling","created":"2014-12-11","cited_by_count":2367},{"id":"1612.06890","title":"CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning","created":"2016-12-20","cited_by_count":1457},{"id":"1611.00712","title":"The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables","created":"2016-11-02","cited_by_count":1148},{"id":"1705.09406","title":"Multimodal Machine Learning: A Survey and Taxonomy","created":"2017-05-26","cited_by_count":1013},{"id":"1712.01815","title":"Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm","created":"2017-12-05","cited_by_count":734},{"id":"2205.12548","title":"RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning","created":"2022-05-25","cited_by_count":366},{"id":"1910.05789","title":"On the Utility of Learning about Humans for Human-AI Coordination","created":"2019-10-13","cited_by_count":349},{"id":"1810.12890","title":"DropBlock: A regularization method for convolutional networks","created":"2018-10-30","cited_by_count":262},{"id":"1810.11187","title":"TarMAC: Targeted Multi-Agent Communication","created":"2018-10-26","cited_by_count":208},{"id":"1612.07182","title":"Multi-Agent Cooperation and the Emergence of (Natural) Language","created":"2016-12-21","cited_by_count":185},{"id":"2006.02419","title":"Emergent Multi-Agent Communication in the Deep Learning Era","created":"2020-06-03","cited_by_count":170},{"id":"1705.11192","title":"Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols","created":"2017-05-31","cited_by_count":125},{"id":"2004.09124","title":"Compositionality and Generalization in Emergent Languages","created":"2020-04-20","cited_by_count":103},{"id":"1804.03984","title":"Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel Input","created":"2018-04-11","cited_by_count":102},{"id":"1706.08502","title":"Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog","created":"2017-06-26","cited_by_count":86},{"id":"1902.07181","title":"Measuring Compositionality in Representation Learning","created":"2019-02-19","cited_by_count":84},{"id":"2002.01365","title":"Compositional Languages Emerge in a Neural Iterated Learning Model","created":"2020-02-04","cited_by_count":75},{"id":"1906.02403","title":"Ease-of-Teaching and Language Structure from Emergent Communication","created":"2019-06-06","cited_by_count":65},{"id":"1905.12561","title":"Anti-efficient encoding in emergent communication","created":"2019-05-29","cited_by_count":59},{"id":"2003.12694","title":"Countering Language Drift with Seeded Iterated Learning","created":"2020-03-28","cited_by_count":57},{"id":"1808.10696","title":"How agents see things: On visual representations in an emergent language game","created":"2018-08-31","cited_by_count":45},{"id":"1907.00852","title":"EGG: a toolkit for research on Emergence of lanGuage in Games","created":"2019-07-01","cited_by_count":42},{"id":"1901.08706","title":"Emergent Linguistic Phenomena in Multi-Agent Communication Games","created":"2019-01-25","cited_by_count":36},{"id":"1910.11424","title":"Capacity, Bandwidth, and Compositionality in Emergent Language Learning","created":"2019-10-24","cited_by_count":30},{"id":"2209.15342","title":"Emergent Communication: Generalization and Overfitting in Lewis Games","created":"2022-09-30","cited_by_count":28},{"id":"2106.04258","title":"Interpretable agent communication from scratch (with a generic visual processor emerging on the side)","created":"2021-06-08","cited_by_count":25},{"id":"2204.12982","title":"On the role of population heterogeneity in emergent communication","created":"2022-04-27","cited_by_count":24},{"id":"2010.01878","title":"\"LazImpa\": Lazy and Impatient neural agents learn to communicate efficiently","created":"2020-10-05","cited_by_count":23},{"id":"2204.10590","title":"Emergent Communication for Understanding Human Language Evolution: What's Missing?","created":"2022-04-22","cited_by_count":23},{"id":"2203.13344","title":"Linking Emergent and Natural Languages via Corpus Transfer","created":"2022-03-24","cited_by_count":19},{"id":"2010.15896","title":"Exploring Zero-Shot Emergent Communication in Embodied Multi-Agent Populations","created":"2020-10-29","cited_by_count":18},{"id":"1904.09067","title":"Emergence of Compositional Language with Deep Generational Transmission","created":"2019-04-19","cited_by_count":17},{"id":"1912.06208","title":"Shaping representations through communication: community size effect in artificial learning systems","created":"2019-12-12","cited_by_count":14},{"id":"2212.03419","title":"JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset","created":"2022-12-07","cited_by_count":11},{"id":"2211.01480","title":"Over-communicate no more: Situated RL agents learn concise communication protocols","created":"2022-11-02","cited_by_count":4}],"next_cursor":null}