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10/08/2021
Neural Tangent Kernel Eigenvalues Accurately Predict Generalization
Finding a quantitative theory of neural network generalization has long ...
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06/06/2021
On the Power of Shallow Learning
A deluge of recent work has explored equivalences between wide neural ne...
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07/17/2020
A new method for parameter estimation in probabilistic models: Minimum probability flow
Fitting probabilistic models to data is often difficult, due to the gene...
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03/23/2020
Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses
Despite the fact that the loss functions of deep neural networks are hig...
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12/13/2019
Design of optical neural networks with component imprecisions
For the benefit of designing scalable, fault resistant optical neural ne...
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01/29/2019
Numerically Recovering the Critical Points of a Deep Linear Autoencoder
Numerically locating the critical points of non-convex surfaces is a lon...
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09/13/2015
A Markov Jump Process for More Efficient Hamiltonian Monte Carlo
In most sampling algorithms, including Hamiltonian Monte Carlo, transiti...
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04/18/2015
