Jacob Andreas

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	总计	2018 年至今
引用	8429	7599
h 指数	42	40
i10 指数	70	66

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Trevor DarrellProfessor of Computer Science, U.C. Berkeley在 eecs.berkeley.edu 的电子邮件经过验证
Ekin AkyürekPhD Student in Computer Science, MIT在 mit.edu 的电子邮件经过验证
Marcus RohrbachProfessor for Multimodal Reliable AI, TU Darmstadt, Germany在 tu-darmstadt.de 的电子邮件经过验证
Kate SaenkoBoston University在 bu.edu 的电子邮件经过验证
Ronghang HuResearch Scientist, Meta AI在 meta.com 的电子邮件经过验证
Antonio TorralbaProfessor of Computer Science, MIT在 csail.mit.edu 的电子邮件经过验证
Taylor Berg-KirkpatrickUniversity of California San Diego在 ucsd.edu 的电子邮件经过验证
Adam PaulsMicrosoft在 semanticmachines.com 的电子邮件经过验证
Pratyusha SharmaPhD Student, Massachusetts Institute of Technology在 mit.edu 的电子邮件经过验证
Sergey LevineUC Berkeley, Google在 eecs.berkeley.edu 的电子邮件经过验证
Belinda Z. LiMIT在 mit.edu 的电子邮件经过验证
Daniel FriedCarnegie Mellon University在 cs.cmu.edu 的电子邮件经过验证
Joshua B. TenenbaumMIT在 mit.edu 的电子邮件经过验证
Kathleen McKeownProfessor of Computer Science and Director, Data Science Institute, Columbia University在 cs.columbia.edu 的电子邮件经过验证
Sara RosenthalIBM Research在 cs.columbia.edu 的电子邮件经过验证
Daniel BauerComputer Science, Columbia University在 cs.columbia.edu 的电子邮件经过验证
Karl Moritz HermannSaiga在 google.com 的电子邮件经过验证
Kevin KnightDiDi Labs在 didiglobal.com 的电子邮件经过验证
Aleksandr NisnevichSemantic Machines在 semanticmachines.com 的电子邮件经过验证
Larry GillickPartner Principal Researcher, Microsoft在 microsoft.com 的电子邮件经过验证

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Jacob Andreas

MIT

在 mit.edu 的电子邮件经过验证 - 首页

NLP ML


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Neural module networks J Andreas, M Rohrbach, T Darrell, D Klein CVPR, 2016	1267	2016
Learning to Compose Neural Networks for Question Answering J Andreas, M Rohrbach, T Darrell, D Klein NAACL, 2016	640	2016
Learning to reason: End-to-end module networks for visual question answering R Hu, J Andreas, M Rohrbach, T Darrell, K Saenko ICCV, 2017	631	2017
Modular multitask reinforcement learning with policy sketches J Andreas, D Klein, S Levine ICML, 2017	494	2017
Speaker-follower models for vision-and-language navigation D Fried, R Hu, V Cirik, A Rohrbach, J Andreas, LP Morency, ... NeurIPS, 2018	438	2018
Modeling relationships in referential expressions with compositional modular networks R Hu, M Rohrbach, J Andreas, T Darrell, K Saenko CVPR, 2017	380	2017
Experience grounds language Y Bisk, A Holtzman, J Thomason, J Andreas, Y Bengio, J Chai, M Lapata, ... EMNLP, 2020	327	2020
A survey of reinforcement learning informed by natural language J Luketina, N Nardelli, G Farquhar, J Foerster, J Andreas, E Grefenstette, ... IJCAI, 2019	255	2019
Good-enough compositional data augmentation J Andreas EMNLP, 2020	220	2020
A minimal span-based neural constituency parser M Stern, J Andreas, D Klein ACL, 2017	208	2017
Explainable neural computation via stack neural module networks R Hu, J Andreas, T Darrell, K Saenko ECCV, 2018	190	2018
Reasoning about pragmatics with neural listeners and speakers J Andreas, D Klein EMNLP, 2016	171	2016
Semantics-Based Machine Translation with Hyperedge Replacement Grammars B Jones, J Andreas, D Bauer, KM Hermann, K Knight COLING, 2012	157	2012
What learning algorithm is in-context learning? investigations with linear models E Akyürek, D Schuurmans, J Andreas, T Ma, D Zhou ICLR, 2023	146	2023
Measuring compositionality in representation learning J Andreas ICLR, 2019	140	2019
A benchmark for systematic generalization in grounded language understanding L Ruis, J Andreas, M Baroni, D Bouchacourt, BM Lake NeurIPS, 2020	131	2020
Learning with latent language J Andreas, D Klein, S Levine NAACL, 2018	123	2018
Compositional explanations of neurons J Mu, J Andreas NeurIPS, 2020	121	2020
Pre-trained language models for interactive decision-making YZ Shuang Li, Xavier Puig, Chris Paxton, Yilun Du, Clinton Wang, Linxi Fan ... NeurIPS, 2022	109*	2022
Alignment-based compositional semantics for instruction following J Andreas, D Klein EMNLP, 2015	106	2015

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