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Song Jiang

6 papers hereh-index 8541 citations15 works total

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

author position
  • first author1
  • middle author5

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.AI1
  • stat.ML1
same name
  • Song Jiang — 43 papers, h 44
  • Song Jiang — 13 papers, h 9
  • Song Jiang — 6 papers, h 1
  • Song Jiang — 4 papers, h 7
  • Song Jiang — 3 papers, h 2
  • Song Jiang — 2 papers, h 4

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
20192025
most citedLayer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks

87 citations · 88 across the 6 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2025

FUSE: Measure-Theoretic Compact Fuzzy Set Representation for Taxonomy Expansion

Fred Xu, Song Jiang, Zijie Huang +4

Taxonomy Expansion, which models complex concepts and their relations, can be formulated as a set representation learning task. The generalization of set, fuzzy set, incorporates u…

cs.LG2023

CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systems

Song Jiang, Zijie Huang, Xiao Luo +1

Multi-agent dynamical systems refer to scenarios where multiple units interact with each other and evolve collectively over time. To make informed decisions in multi-agent dynamica…

cs.LG2021

A Primer on Deep Learning for Causal Inference

Bernard Koch, Tim Sainburg, Pablo Geraldo +3

This review systematizes the emerging literature for causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction on ho…

cs.LG2019★ 87 cited

Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks

Difan Zou, Ziniu Hu, Yewen Wang +3

Graph convolutional networks (GCNs) have recently received wide attentions, due to their successful applications in different graph tasks and different domains. Training GCNs for a…

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