3 papers
cs.LG2026
Separating Intrinsic Ambiguity from Estimation Uncertainty in Deep Generative Models for Linear Inverse Problems
Yuxin Guo, Dongrui Deng, Pulkit Grover
Recently, deep generative models have been used for posterior inference in inverse problems, including high-stakes applications in medical imaging and scientific discovery, where t…
cs.LG2026
Enjoy Your Layer Normalization with the Computational Efficiency of RMSNorm
Yuxin Guo, Yihao Yue, Yunhao Ni +4
Layer normalization (LN) is a fundamental component in modern deep learning, but its per-sample centering and scaling introduce non-negligible inference overhead. RMSNorm improves…
cs.LG2026
Parallel Layer Normalization for Universal Approximation
Yunhao Ni, Yuxin Guo, Yuhe Liu +4
This paper studies the approximation capabilities of neural networks that combine layer normalization (LN) with linear layers. We prove that networks consisting of two linear layer…