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Mayank Goswami

26 papers hereh-index 191.4k citations78 works total

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

author position
  • sole author1
  • first author6
  • middle author16
  • last author2

Across the 25 of 26 papers where every author was matched, so the position is known.

fields
  • cs.DS12
  • cs.LG4
  • cs.CG3
  • cs.CV2
  • eess.IV2
  • eess.SP2
same name
  • Mayank Goswami — 7 papers, h 2
  • Mayank Goswami — 3 papers, h 2
  • Mayank Goswami — 3 papers, h 2
  • Mayank Goswami — 1 paper, h 1

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
20142023
most citedError-Bounded Correction of Noisy Labels

40 citations · 168 across the 23 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2022★ 1 cited

A Manifold View of Adversarial Risk

Wenjia Zhang, Yikai Zhang, Xiaoling Hu +3

The adversarial risk of a machine learning model has been widely studied. Most previous works assume that the data lies in the whole ambient space. We propose to take a new angle a…

cs.LG2021★ 2 cited

Topological Detection of Trojaned Neural Networks

Songzhu Zheng, Yikai Zhang, Hubert Wagner +2

Deep neural networks are known to have security issues. One particular threat is the Trojan attack. It occurs when the attackers stealthily manipulate the model's behavior through…

cs.LG2021★ 32 cited

Learning with Feature-Dependent Label Noise: A Progressive Approach

Yikai Zhang, Songzhu Zheng, Pengxiang Wu +2

Label noise is frequently observed in real-world large-scale datasets. The noise is introduced due to a variety of reasons; it is heterogeneous and feature-dependent. Most existing…

cs.LG2021★ 4 cited

Stability of SGD: Tightness Analysis and Improved Bounds

Yikai Zhang, Wenjia Zhang, Sammy Bald +3

Stochastic Gradient Descent (SGD) based methods have been widely used for training large-scale machine learning models that also generalize well in practice. Several explanations h…

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