4 papers
Relative Repairability: A Calibration-Based Diagnostic for High-Sparsity Post-Pruning Allocation
Qishi Zhan, Liang He, Minxuan Hu +1
At very high sparsity, neural network pruning does more than decide which weights remain. It also determines where pruning induced damage is placed across the network, and whether…
How Sparsity Allocation Shapes Label-Free Post-Pruning Recoverability
Qishi Zhan, Minxuan Hu, Liang He
Unstructured magnitude pruning at high sparsity can reduce neural network accuracy to near-random performance, while labeled retraining may be unavailable in practical deployment s…
A Tale of Two Variances: When Single-Seed Benchmarks Fail in Bayesian Deep Learning
Qishi Zhan, Minxuan Hu, Liang He +2
In limited-data settings, a single endpoint mean of an evaluation metric such as the Continuous Ranked Probability Score (CRPS) is itself a random variable, yet it is routinely rep…
Unstable Rankings in Bayesian Deep Learning Evaluation
Qishi Zhan, Minxuan Hu, Guansu Wang +2
Standard evaluations of Bayesian deep learning methods assume that metric estimates are reliable, but we show this assumption fails under data scarcity. Method rankings are not onl…