Machine Intelligence Reading Group Discussion | Joseph KJ

Joseph KJ  
Machine Intelligence group is glad to announce Reading Groups session with Joseph KJ (CS Ph.D.), as we discuss about his recent work on Meta-Consolidation for Continual Learning published in NeurIPS 2020.

The ability to continuously learn and adapt itself to new tasks, without losing grasp of already acquired knowledge is a hallmark of biological learning systems, which current deep learning systems fall short of. In this work, we present a novel methodology for continual learning called MERLIN: Meta-Consolidation for Continual Learning. We assume that the weights of a neural network, for solving tasks, come from a meta-distribution. This meta-distribution is learned and consolidated incrementally. We operate in the challenging online continual learning setting, where a data point is seen by the model only once. Our experiments with continual learning benchmarks of MNIST, CIFAR-10, CIFAR-100, and Mini-ImageNet datasets show consistent improvement over five baselines, including a recent state-of-the-art, corroborating the promise of MERLIN.

 Join the Talk on 7th Feb, Sunday 5 PM IST on Google Meet!