2 citations · 2 across the 3 of their papers we have counts for
3 papers · 1 filter
PLATINUM: Semi-Supervised Model Agnostic Meta-Learning using Submodular Mutual Information
Changbin Li, Suraj Kothawade, Feng Chen +1
Few-shot classification (FSC) requires training models using a few (typically one to five) data points per class. Meta learning has proven to be able to learn a parametrized model…
A Nested Bi-level Optimization Framework for Robust Few Shot Learning
Krishnateja Killamsetty, Changbin Li, Chen Zhao +2
Model-Agnostic Meta-Learning (MAML), a popular gradient-based meta-learning framework, assumes that the contribution of each task or instance to the meta-learner is equal. Hence, i…
Fair Meta-Learning For Few-Shot Classification
Chen Zhao, Changbin Li, Jincheng Li +1
Artificial intelligence nowadays plays an increasingly prominent role in our life since decisions that were once made by humans are now delegated to automated systems. A machine le…