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cs.LG2026
Adaptive Momentum and Nonlinear Damping for Neural Network Training
Aikaterini Karoni, Rajit Rajpal, Benedict Leimkuhler +1
Momentum Stochastic Gradient Descent (mSGD) relies on a fixed momentum coefficient shared across all parameters, failing to account for the heterogeneous structure of modern loss l…
cs.LG2025
Adaptive Stepsizing for Stochastic Gradient Langevin Dynamics in Bayesian Neural Networks
Rajit Rajpal, Benedict Leimkuhler, Yuanhao Jiang
Bayesian neural networks (BNNs) require scalable sampling algorithms to approximate posterior distributions over parameters. Existing stochastic gradient Markov Chain Monte Carlo (…