81 citations · 109 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Mixed-norm Regularization for Brain Decoding
Rémi Flamary, Nisrine Jrad, Ronald Phlypo +2
This work investigates the use of mixed-norm regularization for sensor selection in Event-Related Potential (ERP) based Brain-Computer Interfaces (BCI). The classification problem…
Independent Vector Analysis: Identification Conditions and Performance Bounds
Matthew Anderson, Geng-Shen Fu, Ronald Phlypo +1
Recently, an extension of independent component analysis (ICA) from one to multiple datasets, termed independent vector analysis (IVA), has been the subject of significant research…