4 papers
Train for Many, Update with One: Improving Multi-Sample Reasoning via Fenchel-Bregman Posterior Learning
Sungjun Lim, Adrian Robert Minut, Nico Carlsen Daheim +3
Multi-sample reasoning measures candidate coverage, the probability that at least one of candidates is correct. However, minimizing the average loss of individual attempts does…
Fast and Slow Variational Continual Learning
Subarnaduti Paul, Yohan Jung, Mohammad Emtiyaz Khan +3
Continual learning remains a major challenge for modern deep networks, partly because commonly used optimizers lack inherent mechanisms for continual adaptation. One such natural m…
SVRG and Beyond via Posterior Correction
Nico Daheim, Thomas Möllenhoff, Ming Liang Ang +1
Stochastic Variance Reduced Gradient (SVRG) and its variants aim to speed-up training by using gradient corrections. Originally proposed over a decade ago, these methods have never…
Compact Memory for Continual Logistic Regression
Yohan Jung, Hyungi Lee, Wenlong Chen +4
Despite recent progress, continual learning still does not match the performance of batch training. To avoid catastrophic forgetting, we need to build compact memory of essential p…