1 citations · 1 across the 19 of their papers we have counts for
4 papers · 2 filters
Efficiently Training A Flat Neural Network Before It has been Quantizated
Peng Xia, Junbiao Pang, Tianyang Cai
Post-training quantization (PTQ) for vision transformers (ViTs) has garnered significant attention due to its efficiency in compressing models. However, existing methods typically…
Adaptively Sampling-Reusing-Mixing Decomposed Gradients to Speed Up Sharpness Aware Minimization
Jiaxin Deng, Junbiao Pang
Sharpness-Aware Minimization (SAM) improves model generalization but doubles the computational cost of Stochastic Gradient Descent (SGD) by requiring twice the gradient calculation…
MEC-Quant: Maximum Entropy Coding for Extremely Low Bit Quantization-Aware Training
Junbiao Pang, Tianyang Cai, Baochang Zhang
Quantization-Aware Training (QAT) has driven much attention to produce efficient neural networks. Current QAT still obtains inferior performances compared with the Full Precision (…
Stabilizing Quantization-Aware Training by Implicit-Regularization on Hessian Matrix
Junbiao Pang, Tianyang Cai
Quantization-Aware Training (QAT) is one of the prevailing neural network compression solutions. However, its stability has been challenged for yielding deteriorating performances…