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cs.LG2026
General and Efficient Steering of Diffusion Models
Qingsong Wang, Mikhail Belkin, Yusu Wang
Steering diffusion models toward conditions unseen during training typically requires either retraining with conditional inputs or per-step gradient computations, both of which inc…
cs.LG2025
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…
cs.LG2024
More is Better in Modern Machine Learning: when Infinite Overparameterization is Optimal and Overfitting is Obligatory
James B. Simon, Dhruva Karkada, Nikhil Ghosh +1
In our era of enormous neural networks, empirical progress has been driven by the philosophy that more is better. Recent deep learning practice has found repeatedly that larger mod…