From the 1 of 13 linked papers with an AI index.
13 papers
Low-Dimensional High-Leverage Subspace Optimization: Beyond Full-Parameter Coupled Training for Neural Network Quantization
Peng Xia, Junbiao Pang, Zheng Huang
Low-bit quantization suffers severe accuracy degradation on compact networks, rooted in the dominant full-parameter coupled training paradigm that ignores parameter subspace hetero…
On the Implicit Flatness Bias of Sharpness-Aware Minimization: A Linear Stability Analysis with Quantitative Hyperparameter Bounds
Jiaxin Deng, Junbiao Pang
Sharpness-Aware Minimization (SAM) improves generalization by seeking parameters whose loss is robust to local adversarial perturbations, but the quantitative mechanism underlying…
From Perturbation Correction to Geometry-Aware Sampling: Sharpness-Guided Equilibrium Sampling for Balanced Flat Minima in Long-Tailed Learning
Jiaxin Deng, Junbiao Pang
Long-tailed learning couples two sources of poor generalization: head classes dominate training exposure, while under-represented classes often converge to sharper regions of the l…
Gradient-Energy Guided Block-Wise Perturbations for Sharpness-Aware Minimization
Zhen Huang, Jiaxin Deng, Junbiao Pang
Sharpness-Aware Minimization (SAM) improves generalization by minimizing the worst-case loss in a local parameter neighborhood. Standard SAM implicitly allocates its global perturb…
Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models
Peng Xia, Junbiao Pang, Muhammad Ayub Sabir
The paper introduces Efficient Tuning Before Quantization (ETBQ), a lightweight pre‑conditioning step that adjusts a full‑precision model using perturbations from quantization erro…
Adversarial LassoNet: Robust Feature Selection via Stability-Driven Sparse Learning
Zhen Huang, Peicheng Xu, Junbiao Pang +1
Sparse feature selection is critical for high-dimensional machine learning, yet traditional -regularized methods are often brittle under observational noise and spurious co…