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researcher

Yiran Chen

16 papers here

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

author position
  • middle author6
  • last author9

Across the 15 of 16 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.LG4
  • cs.AR3
  • cs.CR2
  • cs.AI1
  • cs.NE1
ORCID 0000-0002-1486-8412
same name
  • Yiran Chen — 40 papers, h 65
  • Yiran Chen — 15 papers, h 26
  • Yiran Chen — 12 papers, h 6
  • Yiran Chen — 8 papers, h 4
  • Yiran Chen — 8 papers, h 7
  • Yiran Chen — 6 papers, h 7

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
20162025
most citedLearning Structured Sparsity in Deep Neural Networks

468 citations · 486 across the 16 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2024

OSR-ViT: A Simple and Modular Framework for Open-Set Object Detection and Discovery

Matthew Inkawhich, Nathan Inkawhich, Hao Yang +3

An object detector's ability to detect and flag \textit{novel} objects during open-world deployments is critical for many real-world applications. Unfortunately, much of the work i…

cs.CV2024

Peeking Behind the Curtains of Residual Learning

Tunhou Zhang, Feng Yan, Hai Li +1

The utilization of residual learning has become widespread in deep and scalable neural nets. However, the fundamental principles that contribute to the success of residual learning…

cs.CV2023

Stable and Causal Inference for Discriminative Self-supervised Deep Visual Representations

Yuewei Yang, Hai Li, Yiran Chen

In recent years, discriminative self-supervised methods have made significant strides in advancing various visual tasks. The central idea of learning a data encoder that is robust…

cs.CV2023

SIO: Synthetic In-Distribution Data Benefits Out-of-Distribution Detection

Jingyang Zhang, Nathan Inkawhich, Randolph Linderman +3

Building up reliable Out-of-Distribution (OOD) detectors is challenging, often requiring the use of OOD data during training. In this work, we develop a data-driven approach which…

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