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researcher

Zhan Gao

University of Pennsylvania

14 papers hereh-index 221.7k citations148 works total

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

author position
  • first author11
  • middle author3

Across the 14 of 14 papers where every author was matched, so the position is known.

fields
  • eess.SP8
  • cs.LG4
  • cs.CV1
  • hep-ph1
affiliations
  • University of Pennsylvania
same name
  • Zhan Gao — 6 papers, h 3
  • Zhan Gao — 5 papers, h 6
  • Zhan Gao — 5 papers, h 1
  • Zhan Gao — 4 papers, h 2
  • Zhan Gao — 1 paper, h 5
  • Zhan Gao — 1 paper, h 7

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
20192023
most citedStochastic Graph Neural Networks

29 citations · 84 across the 14 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2023★ 1 cited

Generalizing Differentially Private Decentralized Deep Learning with Multi-Agent Consensus

Jasmine Bayrooti, Zhan Gao, Amanda Prorok

Cooperative decentralized learning relies on direct information exchange between communicating agents, each with access to locally available datasets. The goal is to agree on model…

cs.LG2021★ 26 cited

Wide and Deep Graph Neural Network with Distributed Online Learning

Zhan Gao, Fernando Gama, Alejandro Ribeiro

Graph neural networks (GNNs) are naturally distributed architectures for learning representations from network data. This renders them suitable candidates for decentralized tasks.…

cs.LG2020★ 1 cited

Spherical Convolutional Neural Networks: Stability to Perturbations in SO(3)

Zhan Gao, Fernando Gama, Alejandro Ribeiro

Spherical convolutional neural networks (Spherical CNNs) learn nonlinear representations from 3D data by exploiting the data structure and have shown promising performance in shape…

cs.LG2020★ 1 cited

Wide and Deep Graph Neural Networks with Distributed Online Learning

Zhan Gao, Fernando Gama, Alejandro Ribeiro

Graph neural networks (GNNs) learn representations from network data with naturally distributed architectures, rendering them well-suited candidates for decentralized learning. Oft…

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