4 citations · 7 across the 3 of their papers we have counts for
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cs.LG2023★ 4 cited
Tune As You Scale: Hyperparameter Optimization For Compute Efficient Training
Abraham J. Fetterman, Ellie Kitanidis, Joshua Albrecht +6
Hyperparameter tuning of deep learning models can lead to order-of-magnitude performance gains for the same amount of compute. Despite this, systematic tuning is uncommon, particul…
cs.LG2022★ 1 cited
On Kernel Regression with Data-Dependent Kernels
James B. Simon
The primary hyperparameter in kernel regression (KR) is the choice of kernel. In most theoretical studies of KR, one assumes the kernel is fixed before seeing the training data. Un…
cs.LG2020
Critical Point-Finding Methods Reveal Gradient-Flat Regions of Deep Network Losses
Charles G. Frye, James Simon, Neha S. Wadia +3
Despite the fact that the loss functions of deep neural networks are highly non-convex, gradient-based optimization algorithms converge to approximately the same performance from m…