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Pin-Yu Chen

165 papers hereh-index 6019.3k citations263 works total

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

author position
  • sole author1
  • first author28
  • middle author115
  • last author20

Across the 164 of 165 papers where every author was matched, so the position is known.

fields
  • cs.LG87
  • cs.CV20
  • stat.ML16
  • cs.SI15
  • cs.CR9
  • cs.CL3
same name
  • Pin-Yu Chen — 24 papers, h 16
  • Pin-Yu Chen — 18 papers, h 9
  • Pin-Yu Chen — 17 papers, h 10
  • Pin-Yu Chen — 14 papers, h 5
  • Pin-Yu Chen — 13 papers, h 12
  • Pin-Yu Chen — 10 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
20142023
most citedZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models

1.8k citations · 5.3k across the 161 of their papers we have counts for

collaborators
Showing 2014Show all

4 papers · 1 filter

cs.SI2014★ 36 cited

Phase Transitions in Spectral Community Detection

Pin-Yu Chen, Alfred O. Hero

Consider a network consisting of two subnetworks (communities) connected by some external edges. Given the network topology, the community detection problem can be cast as a graph…

cs.SI2014★ 26 cited

Universal Phase Transition in Community Detectability under a Stochastic Block Model

Pin-Yu Chen, Alfred O. Hero

We prove the existence of an asymptotic phase transition threshold on community detectability for the spectral modularity method [M. E. J. Newman, Phys. Rev. E 74, 036104 (2006) an…

cs.SI2014★ 55 cited

Deep Community Detection

Pin-Yu Chen, Alfred O. Hero

A deep community in a graph is a connected component that can only be seen after removal of nodes or edges from the rest of the graph. This paper formulates the problem of detectin…

cs.SI2014★ 1 cited

Node Removal Vulnerability of the Largest Component of a Network

Pin-Yu Chen, Alfred O. Hero

The connectivity structure of a network can be very sensitive to removal of certain nodes in the network. In this paper, we study the sensitivity of the largest component size to n…

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