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researcher

N. Zhang

64 papers hereh-index 334.9k citations176 works total

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

author position
  • sole author2
  • first author16
  • middle author11
  • last author34

Across the 63 of 64 papers where every author was matched, so the position is known.

fields
  • cs.LG20
  • cs.AI19
  • cs.CL14
  • cs.IR5
  • cs.CV4
  • cs.OH1
same name
  • N. Zhang — 28 papers
  • N. Zhang — 19 papers, h 21
  • N. Zhang — 11 papers, h 26
  • N. Zhang — 11 papers, h 25
  • N. Zhang — 10 papers, h 63
  • N. Zhang — 8 papers, h 6

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
19962026
most citedIncremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes

345 citations · 840 across the 61 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2026

InSight-doc: Agentic Visual Perception for Long-Document Understanding

Kaican Li, Weiyan Xie, Lewei Yao +4

Long-document understanding often requires reasoning over many visually rich pages, making inference costly and prone to context rot. In this work, we propose InSight-doc, an agent…

cs.CV2023

Two-Stage Holistic and Contrastive Explanation of Image Classification

Weiyan Xie, Xiao-Hui Li, Zhi Lin +3

The need to explain the output of a deep neural network classifier is now widely recognized. While previous methods typically explain a single class in the output, we advocate expl…

cs.CV2022★ 2 cited

ViT-CX: Causal Explanation of Vision Transformers

Weiyan Xie, Xiao-Hui Li, Caleb Chen Cao +1

Despite the popularity of Vision Transformers (ViTs) and eXplainable AI (XAI), only a few explanation methods have been designed specially for ViTs thus far. They mostly use attent…

cs.CV2022★ 2 cited

Deep Clustering with Features from Self-Supervised Pretraining

Xingzhi Zhou, Nevin L. Zhang

A deep clustering model conceptually consists of a feature extractor that maps data points to a latent space, and a clustering head that groups data points into clusters in the lat…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.