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Average gradient outer product as a mechanism for deep neural collapse
In this work, we provide substantial evidence that DNC formation occurs primarily through deep feature learning with the average gradient outer product (AGOP).
The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes
This allows us to improve the generalization of a CNN-based model without any increase in the number of weights at test time.
Generalization In Multi-Objective Machine Learning
Modern machine learning tasks often require considering not just one but multiple objectives.
Intriguing Properties of Input-dependent Randomized Smoothing
We show that in general, the input-dependent smoothing suffers from the curse of dimensionality, forcing the variance function to have low semi-elasticity.
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