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Yikai Wang

Nanyang Technological University

22 papers hereh-index 12553 citations32 works total

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

author position
  • first author6
  • middle author13

Across the 19 of 22 papers where every author was matched, so the position is known.

fields
  • cs.CV16
  • cs.LG4
  • cs.RO2
affiliations
  • Nanyang Technological University
Homepage
same name
  • Yikai Wang — 48 papers, h 18
  • Yikai Wang — 12 papers
  • Yikai Wang — 10 papers, h 3
  • Yikai Wang — 7 papers, h 9
  • Yikai Wang — 7 papers, h 2
  • Yikai Wang — 6 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
20202026
most citedInstance Credibility Inference for Few-Shot Learning

9 citations · 13 across the 21 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2026

Conformal Reliability: A New Evaluation Metric for Conditional Generation

Yachen Gao, Xinwei Sun, Yikai Wang +4

Conditional generative models have recently achieved remarkable success in various applications. However, a suitable metric for evaluating the reliability of these models, which ta…

cs.LG2025

Adaptive Pruning of Pretrained Transformer via Differential Inclusions

Yizhuo Ding, Ke Fan, Yikai Wang +2

Large transformers have demonstrated remarkable success, making it necessary to compress these models to reduce inference costs while preserving their perfor-mance. Current compres…

cs.LG2023

Knockoffs-SPR: Clean Sample Selection in Learning with Noisy Labels

Yikai Wang, Yanwei Fu, Xinwei Sun

A noisy training set usually leads to the degradation of the generalization and robustness of neural networks. In this paper, we propose a novel theoretically guaranteed clean samp…

cs.LG2022

Scalable Penalized Regression for Noise Detection in Learning with Noisy Labels

Yikai Wang, Xinwei Sun, Yanwei Fu

Noisy training set usually leads to the degradation of generalization and robustness of neural networks. In this paper, we propose using a theoretically guaranteed noisy label dete…

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