35 citations · 47 across the 10 of their papers we have counts for
5 papers · 1 filter
Two Speeds of Learning: A Representation-Readout Decomposition of Grokking and Double Descent
Chi-Ning Chou, Oscar Uzdelewicz, Neng-Chun Chiu +2
Training loss and accuracy are the standard signals used to monitor generalization during deep neural network training. Two well-documented phenomena complicate this picture: in gr…
Diagnosing Generalization Failures from Representational Geometry Markers
Chi-Ning Chou, Artem Kirsanov, Yao-Yuan Yang +1
Generalization, the ability to perform well beyond the training context, is a hallmark of biological and artificial intelligence, yet anticipating unseen failures remains a central…
Feature Learning beyond the Lazy-Rich Dichotomy: Insights from Representational Geometry
Chi-Ning Chou, Hang Le, Yichen Wang +1
Integrating task-relevant information into neural representations is a fundamental ability of both biological and artificial intelligence systems. Recent theories have categorized…
Understanding Rare Spurious Correlations in Neural Networks
Yao-Yuan Yang, Chi-Ning Chou, Kamalika Chaudhuri
Neural networks are known to use spurious correlations such as background information for classification. While prior work has looked at spurious correlations that are widespread i…
Scale MLPerf-0.6 models on Google TPU-v3 Pods
Sameer Kumar, Victor Bitorff, Dehao Chen +9
The recent submission of Google TPU-v3 Pods to the industry wide MLPerf v0.6 training benchmark demonstrates the scalability of a suite of industry relevant ML models. MLPerf defin…