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Neural Collapse versus Low-rank Bias: Is Deep Neural Collapse Really Optimal?
Deep neural networks (DNNs) exhibit a surprising structure in their final layer known as neural collapse (NC), and a growing body of works has currently investigated the propagation of neural collapse to earlier layers of DNNs -- a phenomenon called deep neural collapse (DNC).
Average gradient outer product as a mechanism for deep neural collapse
Deep Recursive Feature Machines are a method that constructs a neural network by iteratively mapping the data with the AGOP and applying an untrained random feature map.
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.
