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stat.ML2023
Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte Carlo
Ziyi Wang, Yujie Chen, Qifan Song +1
Low-precision training has emerged as a promising low-cost technique to enhance the training efficiency of deep neural networks without sacrificing much accuracy. Its Bayesian coun…
cs.LG2023
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation
Yue Xiang, Dongyao Zhu, Bowen Lei +2
Gradients have been exploited in proposal distributions to accelerate the convergence of Markov chain Monte Carlo algorithms on discrete distributions. However, these methods requi…
cs.LG2023
Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction
Bowen Lei, Dongkuan Xu, Ruqi Zhang +2
Despite impressive performance, deep neural networks require significant memory and computation costs, prohibiting their application in resource-constrained scenarios. Sparse train…