3 papers
cs.LG2026
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…
cs.LG2026
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…
cs.LG2026
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…