83 citations · 102 across the 11 of their papers we have counts for
5 papers · 1 filter
The Features at Convergence Theorem: a first-principles alternative to the Neural Feature Ansatz for how networks learn representations
Enric Boix-Adsera, Neil Mallinar, James B. Simon +1
It is a central challenge in deep learning to understand how neural networks learn representations. A leading approach is the Neural Feature Ansatz (NFA) (Radhakrishnan et al. 2024…
Minimum-Norm Interpolation Under Covariate Shift
Neil Mallinar, Austin Zane, Spencer Frei +1
Transfer learning is a critical part of real-world machine learning deployments and has been extensively studied in experimental works with overparameterized neural networks. Howev…
The Calibration Generalization Gap
A. Michael Carrell, Neil Mallinar, James Lucas +1
Calibration is a fundamental property of a good predictive model: it requires that the model predicts correctly in proportion to its confidence. Modern neural networks, however, pr…
Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting
Neil Mallinar, James B. Simon, Amirhesam Abedsoltan +3
The practical success of overparameterized neural networks has motivated the recent scientific study of interpolating methods, which perfectly fit their training data. Certain inte…
Iterative Data Programming for Expanding Text Classification Corpora
Neil Mallinar, Abhishek Shah, Tin Kam Ho +2
Real-world text classification tasks often require many labeled training examples that are expensive to obtain. Recent advancements in machine teaching, specifically the data progr…