no code implementations • 12 Feb 2024 • Mikail Khona, Maya Okawa, Jan Hula, Rahul Ramesh, Kento Nishi, Robert Dick, Ekdeep Singh Lubana, Hidenori Tanaka

Stepwise inference protocols, such as scratchpads and chain-of-thought, help language models solve complex problems by decomposing them into a sequence of simpler subproblems.

no code implementations • 21 Nov 2023 • Rahul Ramesh, Ekdeep Singh Lubana, Mikail Khona, Robert P. Dick, Hidenori Tanaka

Transformers trained on huge text corpora exhibit a remarkable set of capabilities, e. g., performing basic arithmetic.

2 code implementations • 2 May 2023 • Jialin Mao, Itay Griniasty, Han Kheng Teoh, Rahul Ramesh, Rubing Yang, Mark K. Transtrum, James P. Sethna, Pratik Chaudhari

We develop information-geometric techniques to analyze the trajectories of the predictions of deep networks during training.

2 code implementations • 31 Oct 2022 • Rahul Ramesh, Jialin Mao, Itay Griniasty, Rubing Yang, Han Kheng Teoh, Mark Transtrum, James P. Sethna, Pratik Chaudhari

We develop information geometric techniques to understand the representations learned by deep networks when they are trained on different tasks using supervised, meta-, semi-supervised and contrastive learning.

1 code implementation • 23 Aug 2022 • Ashwin De Silva, Rahul Ramesh, Carey E. Priebe, Pratik Chaudhari, Joshua T. Vogelstein

In this work, we show a counter-intuitive phenomenon: the generalization error of a task can be a non-monotonic function of the number of OOD samples.

2 code implementations • pproximateinference AABI Symposium 2022 • Yansong Gao, Rahul Ramesh, Pratik Chaudhari

Such priors enable the task to maximally affect the Bayesian posterior, e. g., reference priors depend upon the number of samples available for learning the task and for very small sample sizes, the prior puts more probability mass on low-complexity models in the hypothesis space.

no code implementations • 19 Jan 2022 • Ashwin De Silva, Rahul Ramesh, Lyle Ungar, Marshall Hussain Shuler, Noah J. Cowan, Michael 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 Burns, Onyema Osuagwu, Brett Mensh, Alysson R. Muotri, Julia Brown, Chris White, Weiwei Yang, Andrei A. Rusu, Timothy Verstynen, Konrad P. Kording, Pratik Chaudhari, Joshua T. Vogelstein

We conjecture that certain sequences of tasks are not retrospectively learnable (in which the data distribution is fixed), but are prospectively learnable (in which distributions may be dynamic), suggesting that prospective learning is more difficult in kind than retrospective learning.

no code implementations • ICLR 2022 • Rahul Ramesh, Pratik Chaudhari

This paper argues that continual learning methods can benefit by splitting the capacity of the learner across multiple models.

2 code implementations • 6 Jun 2021 • Rahul Ramesh, Pratik Chaudhari

We use statistical learning theory and experimental analysis to show how multiple tasks can interact with each other in a non-trivial fashion when a single model is trained on them.

Ranked #1 on Continual Learning on Rotated MNIST

no code implementations • 9 Sep 2019 • Arjun Manoharan, Rahul Ramesh, Balaraman Ravindran

Option discovery and skill acquisition frameworks are integral to the functioning of a Hierarchically organized Reinforcement learning agent.

1 code implementation • 14 May 2019 • Rahul Ramesh, Manan Tomar, Balaraman Ravindran

This work adopts a complementary approach, where we attempt to discover options that navigate to landmark states.

no code implementations • 12 Jun 2018 • Revanth Reddy, Rahul Ramesh, Ameet Deshpande, Mitesh M. Khapra

Deep Learning has managed to push boundaries in a wide variety of tasks.

no code implementations • 20 May 2017 • Sahil Sharma, Aravind Suresh, Rahul Ramesh, Balaraman Ravindran

Deep Reinforcement Learning (DRL) methods have performed well in an increasing numbering of high-dimensional visual decision making domains.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.