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Yang Li

University of North Carolina at Chapel Hill

12 papers hereh-index 10537 citations18 works total

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

author position
  • first author5
  • middle author6

Across the 11 of 12 papers where every author was matched, so the position is known.

fields
  • cs.LG8
  • cs.CV2
  • cs.SD1
  • stat.ML1
affiliations
  • University of North Carolina at Chapel Hill
same name
  • Yang Li — 55 papers, h 20
  • Yang Li — 26 papers, h 11
  • Yang Li — 25 papers, h 14
  • Yang Li — 25 papers, h 10
  • Yang Li — 22 papers, h 20
  • Yang Li — 20 papers, h 18

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
20162023
most citedGland Instance Segmentation Using Deep Multichannel Neural Networks

184 citations · 205 across the 11 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.LG2020★ 8 cited

Active Feature Acquisition with Generative Surrogate Models

Yang Li, Junier B. Oliva

Many real-world situations allow for the acquisition of additional relevant information when making an assessment with limited or uncertain data. However, traditional ML approaches…

cs.LG2020★ 10 cited

Exchangeable Neural ODE for Set Modeling

Yang Li, Haidong Yi, Christopher M. Bender +2

Reasoning over an instance composed of a set of vectors, like a point cloud, requires that one accounts for intra-set dependent features among elements. However, since such instanc…

cs.LG2020

Dynamic Feature Acquisition with Arbitrary Conditional Flows

Yang Li, Junier B. Oliva

Many real-world situations allow for the acquisition of additional relevant information when making an assessment with limited or uncertain data. However, traditional ML approaches…

cs.LG2020

Defense Through Diverse Directions

Christopher M. Bender, Yang Li, Yifeng Shi +2

In this work we develop a novel Bayesian neural network methodology to achieve strong adversarial robustness without the need for online adversarial training. Unlike previous effor…

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