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Dawei Zhou

7 papers hereh-index 569 citations9 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author6
  • last author1

Across the 7 of 7 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CV2
  • stat.ML1
same name
  • Dawei Zhou — 15 papers, h 6
  • Dawei Zhou — 8 papers, h 10
  • Dawei Zhou — 3 papers, h 2
  • Dawei Zhou — 3 papers, h 2
  • Dawei Zhou — 3 papers, h 3
  • Dawei Zhou — 2 papers, h 17

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2026

Balancing Learning Rates Across Layers: Exact Two-Step Dynamics and Optimal Scaling in Linear Neural Networks

Tianyu Pang, Vignesh Kothapalli, Shenyang Deng +3

We study optimal learning-rate selection in two-layer and three-layer linear neural networks trained to learn linear target functions. In particular, we derive the exact closed-for…

cs.LG2026

HalluGuard: Demystifying Data-Driven and Reasoning-Driven Hallucinations in LLMs

Xinyue Zeng, Junhong Lin, Yujun Yan +4

The reliability of Large Language Models (LLMs) in high-stakes domains such as healthcare, law, and scientific discovery is often compromised by hallucinations. These failures typi…

cs.LG2024

Causality-Aware Spatiotemporal Graph Neural Networks for Spatiotemporal Time Series Imputation

Baoyu Jing, Dawei Zhou, Kan Ren +1

Spatiotemporal time series are usually collected via monitoring sensors placed at different locations, which usually contain missing values due to various failures, such as mechani…

cs.LG2024

Enhancing Size Generalization in Graph Neural Networks through Disentangled Representation Learning

Zheng Huang, Qihui Yang, Dawei Zhou +1

Although most graph neural networks (GNNs) can operate on graphs of any size, their classification performance often declines on graphs larger than those encountered during trainin…

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