Rahul Ramesh

According to our database, Rahul Ramesh authored at least 20 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Prospective Learning: Learning for a Dynamic Future.

Ashwin De Silva, Rahul Ramesh, Rubing Yang, Siyu Yu, Joshua T. Vogelstein, Pratik Chaudhari

CoRR, 2024

Representation Shattering in Transformers: A Synthetic Study with Knowledge Editing.

Kento Nishi, Maya Okawa, Rahul Ramesh, Mikail Khona, Ekdeep Singh Lubana, Hidenori Tanaka

CoRR, 2024

Many Perception Tasks are Highly Redundant Functions of their Input Data.

Rahul Ramesh, Anthony Bisulco, Ronald W. Ditullio, Linran Wei, Vijay Balasubramanian, Kostas Daniilidis, Pratik Chaudhari

CoRR, 2024

Compositional Capabilities of Autoregressive Transformers: A Study on Synthetic, Interpretable Tasks.

Rahul Ramesh, Ekdeep Singh Lubana, Mikail Khona, Robert P. Dick, Hidenori Tanaka

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Towards an Understanding of Stepwise Inference in Transformers: A Synthetic Graph Navigation Model.

Mikail Khona, Maya Okawa, Jan Hula, Rahul Ramesh, Kento Nishi, Robert P. Dick, Ekdeep Singh Lubana, Hidenori Tanaka

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

How Capable Can a Transformer Become? A Study on Synthetic, Interpretable Tasks.

Rahul Ramesh, Mikail Khona, Robert P. Dick, Hidenori Tanaka, Ekdeep Singh Lubana

CoRR, 2023

The Training Process of Many Deep Networks Explores the Same Low-Dimensional Manifold.

Jialin Mao, Itay Griniasty, Han Kheng Teoh, Rahul Ramesh, Rubing Yang, Mark K. Transtrum, James P. Sethna, Pratik Chaudhari

CoRR, 2023

The Value of Out-of-Distribution Data.

Ashwin De Silva, Rahul Ramesh, Carey E. Priebe, Pratik Chaudhari, Joshua T. Vogelstein

Proceedings of the International Conference on Machine Learning, 2023

A Picture of the Space of Typical Learnable Tasks.

Rahul Ramesh, Jialin Mao, Itay Griniasty, Rubing Yang, Han Kheng Teoh, Mark K. Transtrum, James P. Sethna, Pratik Chaudhari

Proceedings of the International Conference on Machine Learning, 2023

Prospective Learning: Principled Extrapolation to the Future.

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 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

Proceedings of the Conference on Lifelong Learning Agents, 2023

2022

Deep Reference Priors: What is the best way to pretrain a model?

Yansong Gao, Rahul Ramesh, Pratik Chaudhari

Proceedings of the International Conference on Machine Learning, 2022

Model Zoo: A Growing Brain That Learns Continually.

Rahul Ramesh, Pratik Chaudhari

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Boosting a Model Zoo for Multi-Task and Continual Learning.

Rahul Ramesh, Pratik Chaudhari

CoRR, 2021

2020

Option Encoder: A Framework for Discovering a Policy Basis in Reinforcement Learning.

Arjun Manoharan, Rahul Ramesh, Balaraman Ravindran

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

2019

FigureNet : A Deep Learning model for Question-Answering on Scientific Plots.

Revanth Reddy, Rahul Ramesh, Ameet Deshpande, Mitesh M. Khapra

Proceedings of the International Joint Conference on Neural Networks, 2019

Successor Options: An Option Discovery Framework for Reinforcement Learning.

Rahul Ramesh, Manan Tomar, Balaraman Ravindran

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2018

A Question-Answering framework for plots using Deep learning.

Revanth Reddy, Rahul Ramesh, Ameet Deshpande, Mitesh M. Khapra

CoRR, 2018

2017

AUPCR Maximizing Matchings : Towards a Pragmatic Notion of Optimality for One-Sided Preference Matchings.

Girish Raguvir J, Rahul Ramesh, Sachin Sridhar, Vignesh Manoharan

CoRR, 2017

Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning.

Sahil Sharma, Aravind Suresh, Rahul Ramesh, Balaraman Ravindran

CoRR, 2017

2015

Influence of learners' motivation on responses to Facebook promotions of online courses.

Rahul Ramesh, Deepak Gupta

Proceedings of the 2015 International Conference on Advances in Computing, 2015