no code implementations • 13 Oct 2023 • Ramnath Kumar, Anshul Mittal, Nilesh Gupta, Aditya Kusupati, Inderjit Dhillon, Prateek Jain
Such techniques use a two-stage process: (a) contrastive learning to train a dual encoder to embed both the query and documents and (b) approximate nearest neighbor search (ANNS) for finding similar documents for a given query.
no code implementations • 15 Jun 2023 • Ramnath Kumar, Kushal Majmundar, Dheeraj Nagaraj, Arun Sai Suggala
We present Re-weighted Gradient Descent (RGD), a novel optimization technique that improves the performance of deep neural networks through dynamic sample importance weighting.
1 code implementation • 7 Jun 2022 • Ramnath Kumar, Dheeraj Nagaraj
In reinforcement learning (RL), experience replay-based sampling techniques play a crucial role in promoting convergence by eliminating spurious correlations.
1 code implementation • 27 Jan 2022 • Ramnath Kumar, Tristan Deleu, Yoshua Bengio
Recent studies show that task distribution plays a vital role in the meta-learner's performance.
1 code implementation • 27 Jan 2022 • Ramnath Kumar, Tristan Deleu, Yoshua Bengio
Our proposed adversarial training regime for Multi-Task Reinforcement Learning (MT-RL) addresses the limitations of conventional training methods in RL, especially in meta-RL environments where the agent faces new tasks.