35 citations · 47 across the 10 of their papers we have counts for
10 papers
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…
Linear Readout of Neural Manifolds with Continuous Variables
Will Slatton, Chi-Ning Chou, SueYeon Chung
Brains and artificial neural networks compute with continuous variables such as object position or stimulus orientation. However, the complex variability in neural responses makes…
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…
The Geometry of Prompting: Unveiling Distinct Mechanisms of Task Adaptation in Language Models
Artem Kirsanov, Chi-Ning Chou, Kyunghyun Cho +1
Decoder-only language models have the ability to dynamically switch between various computational tasks based on input prompts. Despite many successful applications of prompting, t…
Nonlinear classification of neural manifolds with contextual information
Francesca Mignacco, Chi-Ning Chou, SueYeon Chung
Understanding how neural systems efficiently process information through distributed representations is a fundamental challenge at the interface of neuroscience and machine learnin…
Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds
Michael Kuoch, Chi-Ning Chou, Nikhil Parthasarathy +4
Recently, growth in our understanding of the computations performed in both biological and artificial neural networks has largely been driven by either low-level mechanistic studie…
The Computational Lens: from Quantum Physics to Neuroscience
Chi-Ning Chou
Two transformative waves of computing have redefined the way we approach science. The first wave came with the birth of the digital computer, which enabled scientists to numericall…
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…