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cs.LG2024
Efficient Model Compression for Bayesian Neural Networks
Diptarka Saha, Zihe Liu, Feng Liang
Model Compression has drawn much attention within the deep learning community recently. Compressing a dense neural network offers many advantages including lower computation cost,…
stat.ME2024
Bayesian Smoothing and Feature Selection Using variational Automatic Relevance Determination
Zihe Liu, Diptarka Saha, Feng Liang
This study introduces Variational Automatic Relevance Determination (VARD), a novel approach tailored for fitting sparse additive regression models in high-dimensional settings. VA…
cs.LG2024
NGD converges to less degenerate solutions than SGD
Moosa Saghir, N. R. Raghavendra, Zihe Liu +1
The number of free parameters, or dimension, of a model is a straightforward way to measure its complexity: a model with more parameters can encode more information. However, this…