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cs.LG2026
Increasing Width Allows Greedy Layer-wise Training to Rival End-to-End Backpropagation in Self-Supervised Learning
Syon Mansur, Joel Zylberberg
End-to-end backpropagation has been the dominant mode of training in deep learning, allowing for the coordination of parameter updates across layers of a neural network. Prior stud…
cs.LG2026
Signal-Noise Factorization Isolates Nuisance Variation into Removable Subspaces
Sakin Kirti, Joel Zylberberg
Recent theoretical work identified fundamental properties of representation geometry that shape inference ability of deep neural networks. These include signal-noise factorization…