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researcher

Y. Ma

32 papers hereh-index 284.4k citations168 works total

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

author position
  • first author6
  • middle author16
  • last author9

Across the 31 of 32 papers where every author was matched, so the position is known.

fields
  • cs.CV10
  • cs.IT10
  • cs.LG4
  • eess.SP3
  • cs.CL1
  • cs.CR1
same name
  • Y. Ma — 190 papers, h 44
  • Y. Ma — 179 papers, h 13
  • Y. Ma — 160 papers, h 71
  • Y. Ma — 152 papers
  • Y. Ma — 97 papers, h 9
  • Y. Ma — 69 papers, h 2

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
20172023
most citedSpectral-based Graph Convolutional Network for Directed Graphs

39 citations · 134 across the 19 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2023

Prioritized Trajectory Replay: A Replay Memory for Data-driven Reinforcement Learning

Jinyi Liu, Yi Ma, Jianye Hao +4

In recent years, data-driven reinforcement learning (RL), also known as offline RL, have gained significant attention. However, the role of data sampling techniques in offline RL h…

cs.LG2023★ 23 cited

White-Box Transformers via Sparse Rate Reduction

Yaodong Yu, Sam Buchanan, Druv Pai +5

In this paper, we contend that the objective of representation learning is to compress and transform the distribution of the data, say sets of tokens, towards a mixture of low-dime…

cs.LG2023

Representation Learning via Manifold Flattening and Reconstruction

Michael Psenka, Druv Pai, Vishal Raman +2

This work proposes an algorithm for explicitly constructing a pair of neural networks that linearize and reconstruct an embedded submanifold, from finite samples of this manifold.…

cs.LG2019★ 39 cited

Spectral-based Graph Convolutional Network for Directed Graphs

Yi Ma, Jianye Hao, Yaodong Yang +3

Graph convolutional networks(GCNs) have become the most popular approaches for graph data in these days because of their powerful ability to extract features from graph. GCNs appro…

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