2 citations · 2 across the 9 of their papers we have counts for
9 papers
Deep Simulation-Based Inference for Inhomogeneous Bivariate Log-Gaussian Cox Processes
Qihan Zou, Yan Wang, Tingjin Chu +1
We propose a computationally efficient simulation-based estimation method with a two-step procedure for inhomogeneous bivariate Log-Gaussian Cox Processes. It combines classical Po…
The Curse and Blessing of Mean Bias in FP4-Quantized LLM Training
Hengjie Cao, Zhendong Huang, Mengyi Chen +15
FP4 training promises substantial memory and compute savings for large language models, but remains fragile because blockwise quantization is dictated by extreme activation magnitu…
Multi-Head Attention as a Source of Catastrophic Forgetting in MoE Transformers
Anrui Chen, Ruijun Huang, Xin Zhang +15
Mixture-of-Experts (MoE) architectures are often considered a natural fit for continual learning because sparse routing should localize updates and reduce interference, yet MoE Tra…
SD-MoE: Spectral Decomposition for Effective Expert Specialization
Ruijun Huang, Fang Dong, Xin Zhang +16
Mixture-of-Experts (MoE) architectures scale Large Language Models via expert specialization induced by conditional computation. In practice, however, expert specialization often f…
Spectra: Rethinking Optimizers for LLMs Under Spectral Anisotropy
Zhendong Huang, Hengjie Cao, Fang Dong +14
Gradient signals in LLM training are highly anisotropic: recurrent linguistic structure concentrates energy into a small set of dominant spectral directions, while context specific…
High-dimensional Covariance Estimation by Pairwise Likelihood Truncation
Alessandro Casa, Davide Ferrari, Zhendong Huang
Pairwise likelihood is a useful approximation to the full likelihood function for covariance estimation in high-dimensional context. It simplifies high-dimensional dependencies by…
Fast construction of optimal composite likelihoods
Zhendong Huang, Davide Ferrari
A composite likelihood is a combination of low-dimensional likelihood objects useful in applications where the data have complex structure. Although composite likelihood constructi…
Fast construction of efficient composite likelihood equations
Zhendong Huang, Davide Ferrari
Growth in both size and complexity of modern data challenges the applicability of traditional likelihood-based inference. Composite likelihood (CL) methods address the difficulties…
Parsimonious and powerful composite likelihood testing for group difference and genotype-phenotype association
Zhendong Huang, Davide Ferrari, Guoqi Qian
Testing the association between a phenotype and many genetic variants from case-control data is essential in genome-wide association study (GWAS). This is a challenging task as man…