3 papers
cs.LG2025
Meta-Sparsity: Learning Optimal Sparse Structures in Multi-task Networks through Meta-learning
Richa Upadhyay, Ronald Phlypo, Rajkumar Saini +1
This paper presents meta-sparsity, a framework for learning model sparsity, basically learning the parameter that controls the degree of sparsity, that allows deep neural networks…
cs.LG2024
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…
cs.CV2024
Giving each task what it needs -- leveraging structured sparsity for tailored multi-task learning
Richa Upadhyay, Ronald Phlypo, Rajkumar Saini +1
In the Multi-task Learning (MTL) framework, every task demands distinct feature representations, ranging from low-level to high-level attributes. It is vital to address the specifi…