42 citations · 55 across the 8 of their papers we have counts for
8 papers
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…
Toward Debugging Deep Reinforcement Learning Programs with RLExplorer
Rached Bouchoucha, Ahmed Haj Yahmed, Darshan Patil +4
Deep reinforcement learning (DRL) has shown success in diverse domains such as robotics, computer games, and recommendation systems. However, like any other software system, DRL-ba…
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…
An Empirical Investigation of the Role of Pre-training in Lifelong Learning
Sanket Vaibhav Mehta, Darshan Patil, Sarath Chandar +1
The lifelong learning paradigm in machine learning is an attractive alternative to the more prominent isolated learning scheme not only due to its resemblance to biological learnin…
Disentangling 3D Prototypical Networks For Few-Shot Concept Learning
Mihir Prabhudesai, Shamit Lal, Darshan Patil +3
We present neural architectures that disentangle RGB-D images into objects' shapes and styles and a map of the background scene, and explore their applications for few-shot 3D obje…
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-…