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researcher

P. Gong

4 papers hereh-index 211.4k citations51 works total

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

author position
  • middle author1
  • last author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG2
  • cond-mat.dis-nn1
  • physics.bio-ph1
same name
  • P. Gong — 6 papers, h 19
  • P. Gong — 4 papers, h 35
  • P. Gong — 3 papers, h 97
  • P. Gong — 2 papers, h 27
  • P. Gong — 2 papers, h 3
  • P. Gong — 1 paper, h 19

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

most citedExtended Anderson Criticality in Heavy-Tailed Neural Networks

16 citations · 25 across the 4 of their papers we have counts for

collaborators

4 papers

physics.bio-ph2022★ 7 cited

Self-organization of nonlinearly coupled neural fluctuations into synergistic population codes

Hengyuan Ma, Yang Qi, Pulin Gong +3

Neural activity in the brain exhibits correlated fluctuations that may strongly influence the properties of neural population coding. However, how such correlated neural fluctuatio…

cs.LG2022

Extended critical regimes of deep neural networks

Cheng Kevin Qu, Asem Wardak, Pulin Gong

Deep neural networks (DNNs) have been successfully applied to many real-world problems, but a complete understanding of their dynamical and computational principles is still lackin…

cond-mat.dis-nn2022★ 16 cited

Extended Anderson Criticality in Heavy-Tailed Neural Networks

Asem Wardak, Pulin Gong

We investigate the emergence of complex dynamics in networks with heavy-tailed connectivity by developing a non-Hermitian random matrix theory. We uncover the existence of an exten…

cs.LG2020★ 2 cited

Anomalous diffusion dynamics of learning in deep neural networks

Guozhang Chen, Cheng Kevin Qu, Pulin Gong

Learning in deep neural networks (DNNs) is implemented through minimizing a highly non-convex loss function, typically by a stochastic gradient descent (SGD) method. This learning…

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