3 papers
cs.CV2023
Less is More -- Towards parsimonious multi-task models using structured sparsity
Richa Upadhyay, Ronald Phlypo, Rajkumar Saini +1
Model sparsification in deep learning promotes simpler, more interpretable models with fewer parameters. This not only reduces the model's memory footprint and computational needs…
cs.CV2022
Multi-Task Meta Learning: learn how to adapt to unseen tasks
Richa Upadhyay, Prakash Chandra Chhipa, Ronald Phlypo +2
This work proposes Multi-task Meta Learning (MTML), integrating two learning paradigms Multi-Task Learning (MTL) and meta learning, to bring together the best of both worlds. In pa…
cs.LG2021
Sharing to learn and learning to share; Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning: A meta review
Richa Upadhyay, Ronald Phlypo, Rajkumar Saini +1
Integrating knowledge across different domains is an essential feature of human learning. Learning paradigms such as transfer learning, meta-learning, and multi-task learning refle…