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Ye Wang

9 papers hereh-index 10378 citations14 works total

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

author position
  • first author2
  • middle author3
  • last author4

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.IT2
  • cs.CL1
  • cs.CV1
  • eess.AS1
same name
  • Ye Wang — 24 papers
  • Ye Wang — 17 papers
  • Ye Wang — 6 papers, h 9
  • Ye Wang — 5 papers
  • Ye Wang — 4 papers, h 7
  • Ye Wang — 4 papers

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
20172022
most citedPrivacy-Utility Tradeoffs under Constrained Data Release Mechanisms

28 citations · 61 across the 7 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2021★ 18 cited

CGNN: Traffic Classification with Graph Neural Network

Bo Pang, Yongquan Fu, Siyuan Ren +3

Traffic classification associates packet streams with known application labels, which is vital for network security and network management. With the rise of NAT, port dynamics, and…

cs.LG2021★ 1 cited

STRODE: Stochastic Boundary Ordinary Differential Equation

Hengguan Huang, Hongfu Liu, Hao Wang +2

Perception of time from sequentially acquired sensory inputs is rooted in everyday behaviors of individual organisms. Yet, most algorithms for time-series modeling fail to learn dy…

cs.LG2020★ 8 cited

Deep Graph Random Process for Relational-Thinking-Based Speech Recognition

Hengguan Huang, Fuzhao Xue, Hao Wang +1

Lying at the core of human intelligence, relational thinking is characterized by initially relying on innumerable unconscious percepts pertaining to relations between new sensory s…

cs.LG2018

Invariant Representations from Adversarially Censored Autoencoders

Ye Wang, Toshiaki Koike-Akino, Deniz Erdogmus

We combine conditional variational autoencoders (VAE) with adversarial censoring in order to learn invariant representations that are disentangled from nuisance/sensitive variation…

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