Rahul Ramesh

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Pratik ChaudhariUniversity of Pennsylvania確認したメールアドレス: seas.upenn.edu
Balaraman RavindranProfessor of Computer Science, Indian Institute of Technology Madras確認したメールアドレス: cse.iitm.ac.in
Hidenori TanakaGroup Leader, CBS-NTT Physics of Intelligence Program, Harvard University確認したメールアドレス: fas.harvard.edu
Mikail KhonaResearch Scientist, NVIDIA確認したメールアドレス: nvidia.com
Ekdeep Singh LubanaHarvard / NTT確認したメールアドレス: fas.harvard.edu
Mark TranstrumAssociate Professor of Physics at Brigham Young University確認したメールアドレス: byu.edu
Itay GriniastySchmidt AI PostDoctoral fellow, Cornell University確認したメールアドレス: cornell.edu
James SethnaProfessor of Physics, Cornell University確認したメールアドレス: cornell.edu
Jialin MaoUniversity of Pennsylvania確認したメールアドレス: sas.upenn.edu
Mitesh KhapraIndian Institute of Technology Madras確認したメールアドレス: cse.iitm.ac.in
Ameet DeshpandePrinceton University確認したメールアドレス: princeton.edu
Teoh Han KhengCornell University確認したメールアドレス: cornell.edu
Aravind SureshSoftware Engineer確認したメールアドレス: openai.com
Joshua T. VogelsteinDept of Biomedical Engineering, Johns Hopkins University確認したメールアドレス: jhu.edu
Ashwin De SilvaPhD Student, Johns Hopkins University確認したメールアドレス: jhu.edu
Carey E. PriebeProfessor of Applied Mathematics and Statistics, Johns Hopkins University確認したメールアドレス: jhu.edu
Kento NishiHarvard University確認したメールアドレス: college.harvard.edu
Yansong GaoTikTok | University of Pennsylvania, SJTU確認したメールアドレス: sas.upenn.edu
Vijay BalasubramanianUniversity of Pennsylvania確認したメールアドレス: physics.upenn.edu
Anthony BisulcoUniversity of Pennsylvania確認したメールアドレス: seas.upenn.edu

フォロー

Rahul Ramesh

University of Pennsylvania

確認したメールアドレス: seas.upenn.edu

Machine Learning Deep learning


タイトル引用回数順公開年順タイトル順	引用先引用先	年
Model Zoo: A Growing "Brain" That Learns Continually R Ramesh, P Chaudhari International Conference on Learning Representations (ICLR 22), 2022	78	2022
Successor Options: An Option Discovery Framework for Reinforcement Learning R Ramesh, M Tomar, B Ravindran International Joint Conference on Artificial Intelligence (IJCAI 19), 2019	43	2019
Learning to factor policies and action-value functions: Factored action space representations for deep reinforcement learning S Sharma, A Suresh, R Ramesh, B Ravindran arXiv preprint arXiv:1705.07269, 2017	38	2017
FigureNet: A Deep Learning model for Question-Answering on Scientific Plots R Reddy, R Ramesh, A Deshpande, MM Khapra International Joint Conference on Neural Networks (IJCNN 19), 2019	37*	2019
Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks R Ramesh, ES Lubana, M Khona, RP Dick, H Tanaka Forty-first International Conference on Machine Learning, 0	17*
The training process of many deep networks explores the same low-dimensional manifold J Mao, I Griniasty, HK Teoh, R Ramesh, R Yang, MK Transtrum, ... Proceedings of the National Academy of Sciences 121 (12), e2310002121, 2024	15	2024
The Value of Out-of-Distribution Data A De Silva, R Ramesh*, CE Priebe, P Chaudhari, JT Vogelstein International Conference on Machine Learning (ICML 23), 2022	13	2022
Prospective Learning: Principled Extrapolation to the Future A De Silva, R Ramesh, L Ungar, MH Shuler, NJ Cowan, M Platt, C Li, ... Conference on Lifelong Learning Agents, 347-357, 2023	10*	2023
A picture of the space of typical learnable tasks R Ramesh, J Mao, I Griniasty, R Yang, HK Teoh, M Transtrum, JP Sethna, ... International Conference on Machine Learning (ICML 23), 2022	6	2022
Deep Reference Priors: What is the best way to pretrain a model? Y Gao, R Ramesh, P Chaudhari International Conference on Machine Learning (ICML 22), 2022	6	2022
Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning R Ramesh, A Manoharan, B Ravindran The European Conference on Machine Learning and Principles and Practice of …, 2020	5*	2020
Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model M Khona, M Okawa, J Hula, R Ramesh, K Nishi, R Dick, ES Lubana, ... arXiv preprint arXiv:2402.07757, 2024	2	2024
Prospective Learning: Learning for a Dynamic Future A De Silva, R Ramesh, R Yang*, S Yu, JT Vogelstein, P Chaudhari arXiv preprint arXiv:2411.00109, 2024		2024
Representation Shattering in Transformers: A Synthetic Study with Knowledge Editing K Nishi, M Okawa, R Ramesh, M Khona, H Tanaka, ES Lubana arXiv preprint arXiv:2410.17194, 2024		2024
Many Perception Tasks are Highly Redundant Functions of their Input Data R Ramesh, A Bisulco, RW DiTullio, L Wei, V Balasubramanian, ... arXiv preprint arXiv:2407.13841, 2024		2024
AUPCR Maximizing Matchings: Towards a Pragmatic Notion of Optimality for One-Sided Preference Matchings G Raguvir J, R Ramesh, S Sridhar, V Manoharan Multidisciplinary Workshop on Advances in Preference Handling (AAAI 2018), 2017		2017

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