Rahul Ramesh

30 papers A* 9B 1Journal 16Unranked 4
YearRankTypeTitle / Venue / Authors
2025 — conf
Federated and Continual Learning of AI Models from Routine Clinical Data Under Privacy Constraints.
EMA4MICCAI
Varghese Alex Kollerathu, Vineet Vinay Bhombore, Rahul Ramesh, Abhinandan Tejani, Matthias Wolf, Gerardo Hermosillo Valadez
2025 J jnl
From Linearity to Non-Linearity: How Masked Autoencoders Capture Spatial Correlations.
CoRR
Anthony Bisulco, Rahul Ramesh, Randall Balestriero, Pratik Chaudhari
2025 A* conf
Representation Shattering in Transformers: A Synthetic Study with Knowledge Editing.
ICML
Kento Nishi, Rahul Ramesh, Maya Okawa, Mikail Khona, Hidenori Tanaka, Ekdeep Singh Lubana
2024 A* conf
Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks.
ICML
Rahul Ramesh, Ekdeep Singh Lubana, Mikail Khona, Robert P. Dick, Hidenori Tanaka
2024 J jnl
Many Perception Tasks are Highly Redundant Functions of their Input Data.
CoRR
Rahul Ramesh, Anthony Bisulco, Ronald W. Ditullio, Linran Wei, Vijay Balasubramanian, Kostas Daniilidis, Pratik Chaudhari
2024 A* conf
Prospective Learning: Learning for a Dynamic Future.
NeurIPS
Ashwin De Silva, Rahul Ramesh, Rubing Yang, Siyu Yu, Joshua T. Vogelstein, Pratik Chaudhari
2024 J jnl
Prospective Learning: Learning for a Dynamic Future.
CoRR
Ashwin De Silva, Rahul Ramesh, Rubing Yang, Siyu Yu, Joshua T. Vogelstein, Pratik Chaudhari
2024 J jnl
Representation Shattering in Transformers: A Synthetic Study with Knowledge Editing.
CoRR
Kento Nishi, Maya Okawa, Rahul Ramesh, Mikail Khona, Ekdeep Singh Lubana, Hidenori Tanaka
2024 A* conf
Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model.
ICML
Mikail Khona, Maya Okawa, Jan Hula, Rahul Ramesh, Kento Nishi, Robert P. Dick, Ekdeep Singh Lubana, Hidenori Tanaka
2024 J jnl
Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model.
CoRR
Mikail Khona, Maya Okawa, Jan Hula, Rahul Ramesh, Kento Nishi, Robert P. Dick, Ekdeep Singh Lubana, Hidenori Tanaka
2023 A* conf
A Picture of the Space of Typical Learnable Tasks.
ICML
Rahul Ramesh, Jialin Mao, Itay Griniasty, Rubing Yang, Han Kheng Teoh, Mark K. Transtrum, James P. Sethna, Pratik Chaudhari
2023 J jnl
How Capable Can a Transformer Become? A Study on Synthetic, Interpretable Tasks.
CoRR
Rahul Ramesh, Mikail Khona, Robert P. Dick, Hidenori Tanaka, Ekdeep Singh Lubana
2023 — conf
Prospective Learning: Principled Extrapolation to the Future.
CoLLAs
Ashwin De Silva, Rahul Ramesh, Lyle H. Ungar, Marshall G. Hussain Shuler, Noah J. Cowan, Michael L. Platt, Chen Li, Leyla Isik, Seung-Eon Roh, Adam S. Charles, Archana Venkataraman, Brian Caffo, Javier J. How, Justus M. Kebschull, John W. Krakauer, Maxim Bichuch, Kaleab Alemayehu Kinfu, Eva Yezerets, Dinesh Jayaraman, Jong M. Shin, Soledad Villar, Ian Phillips, Carey E. Priebe, Thomas Hartung, Michael I. Miller, Jayanta Dey, Ningyuan Huang, Eric Eaton, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Randal C. Burns, Onyema Osuagwu, Brett Mensh, Alysson R. Muotri, Julia Brown, Chris White, Weiwei Yang, Andrei A. Rusu, Timothy D. Verstynen, Konrad P. Kording, Pratik Chaudhari, Joshua T. Vogelstein
2023 J jnl
The Training Process of Many Deep Networks Explores the Same Low-Dimensional Manifold.
CoRR
Jialin Mao, Itay Griniasty, Han Kheng Teoh, Rahul Ramesh, Rubing Yang, Mark K. Transtrum, James P. Sethna, Pratik Chaudhari
2023 A* conf
The Value of Out-of-Distribution Data.
ICML
Ashwin De Silva, Rahul Ramesh, Carey E. Priebe, Pratik Chaudhari, Joshua T. Vogelstein
2022 J jnl
A picture of the space of typical learnable tasks.
CoRR
Rahul Ramesh, Jialin Mao, Itay Griniasty, Rubing Yang, Han Kheng Teoh, Mark K. Transtrum, James P. Sethna, Pratik Chaudhari
2022 A* conf
Deep Reference Priors: What is the best way to pretrain a model?
ICML
Yansong Gao, Rahul Ramesh, Pratik Chaudhari
2022 J jnl
Deep Reference Priors: What is the best way to pretrain a model?
CoRR
Yansong Gao, Rahul Ramesh, Pratik Chaudhari
2022 A* conf
Model Zoo: A Growing Brain That Learns Continually.
ICLR
Rahul Ramesh, Pratik Chaudhari
2022 J jnl
The Value of Out-of-Distribution Data.
CoRR
Ashwin De Silva, Rahul Ramesh, Carey E. Priebe, Pratik Chaudhari, Joshua T. Vogelstein
2021 J jnl
Boosting a Model Zoo for Multi-Task and Continual Learning.
CoRR
Rahul Ramesh, Pratik Chaudhari
2020 — conf
Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning.
ECML/PKDD (2)
Arjun Manoharan, Rahul Ramesh, Balaraman Ravindran
2019 B conf
FigureNet : A Deep Learning model for Question-Answering on Scientific Plots.
IJCNN
Revanth Reddy, Rahul Ramesh, Ameet Deshpande, Mitesh M. Khapra
2019 J jnl
Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning.
CoRR
Arjun Manoharan, Rahul Ramesh, Balaraman Ravindran
2019 A* conf
Successor Options: An Option Discovery Framework for Reinforcement Learning.
IJCAI
Rahul Ramesh, Manan Tomar, Balaraman Ravindran
2019 J jnl
Successor Options: An Option Discovery Framework for Reinforcement Learning.
CoRR
Rahul Ramesh, Manan Tomar, Balaraman Ravindran
2018 J jnl
A Question-Answering framework for plots using Deep learning.
CoRR
Revanth Reddy, Rahul Ramesh, Ameet Deshpande, Mitesh M. Khapra
2017 J jnl
AUPCR Maximizing Matchings : Towards a Pragmatic Notion of Optimality for One-Sided Preference Matchings.
CoRR
Girish Raguvir J, Rahul Ramesh, Sachin Sridhar, Vignesh Manoharan
2017 J jnl
Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning.
CoRR
Sahil Sharma, Aravind Suresh, Rahul Ramesh, Balaraman Ravindran
2015 — conf
Influence of learners' motivation on responses to Facebook promotions of online courses.
ICACCI
Rahul Ramesh, Deepak Gupta