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researcher

Wei Chen

13 papers hereh-index 171.8k citations44 works total

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

author position
  • middle author11
  • last author1

Across the 12 of 13 papers where every author was matched, so the position is known.

fields
  • cs.LG11
  • cs.CV1
  • stat.ML1
same name
  • Wei Chen — 45 papers, h 32
  • Wei Chen — 45 papers, h 35
  • Wei Chen — 45 papers, h 56
  • Wei Chen — 34 papers, h 21
  • Wei Chen — 33 papers
  • Wei Chen — 30 papers, h 25

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 citedR-Drop: Regularized Dropout for Neural Networks

306 citations · 332 across the 9 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.CV2020★ 9 cited

Identifying Invariant Texture Violation for Robust Deepfake Detection

Xinwei Sun, Botong Wu, Wei Chen

Existing deepfake detection methods have reported promising in-distribution results, by accessing published large-scale dataset. However, due to the non-smooth synthesis method, th…

cs.LG2020

Latent Causal Invariant Model

Xinwei Sun, Botong Wu, Xiangyu Zheng +4

Current supervised learning can learn spurious correlation during the data-fitting process, imposing issues regarding interpretability, out-of-distribution (OOD) generalization, an…

stat.ML2020

Learning Causal Semantic Representation for Out-of-Distribution Prediction

Chang Liu, Xinwei Sun, Jindong Wang +5

Conventional supervised learning methods, especially deep ones, are found to be sensitive to out-of-distribution (OOD) examples, largely because the learned representation mixes th…

cs.LG2020

Dynamic of Stochastic Gradient Descent with State-Dependent Noise

Qi Meng, Shiqi Gong, Wei Chen +2

Stochastic gradient descent (SGD) and its variants are mainstream methods to train deep neural networks. Since neural networks are non-convex, more and more works study the dynamic…

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