11 citations · 11 across the 6 of their papers we have counts for
5 papers · 1 filter
Loss Smoothing for Stable Adaptation Under Distribution Shift
Darshan Patil, Ekaterina Lobacheva, Razvan Pascanu +1
In settings such as fine-tuning and reinforcement learning, neural networks are often adapted under distribution shift. Standard adaptation methods typically optimize the target ob…
CoPeP: Benchmarking Continual Pretraining for Protein Language Models
Darshan Patil, Pranshu Malviya, Mathieu Reymond +2
Protein language models (pLMs) have recently gained significant attention for their ability to uncover relationships between sequence, structure, and function from evolutionary sta…
Position: Modular Memory is the Key to Continual Learning Agents
Vaggelis Dorovatas, Malte Schwerin, Andrew D. Bagdanov +21
Foundation models have transformed machine learning through large-scale pretraining and increased test-time compute. Despite surpassing human performance in several domains, these…
Intelligent Switching for Reset-Free RL
Darshan Patil, Janarthanan Rajendran, Glen Berseth +1
In the real world, the strong episode resetting mechanisms that are needed to train agents in simulation are unavailable. The \textit{resetting} assumption limits the potential of…
Towards modular and programmable architecture search
Renato Negrinho, Darshan Patil, Nghia Le +3
Neural architecture search methods are able to find high performance deep learning architectures with minimal effort from an expert. However, current systems focus on specific use-…