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Usman Roshan

New Jersey Institute of Technology

8 papers hereh-index 161.3k citations70 works total

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

author position
  • middle author2
  • last author6

Across the 8 of 8 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CV2
  • eess.IV1
  • q-bio.GN1
affiliations
  • New Jersey Institute of Technology
Homepage
same name
  • Usman Roshan — 4 papers, h 2

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
20142020
most citedMaxSSmap: A GPU program for mapping divergent short reads to genomes with the maximum scoring subsequence

9 citations · 22 across the 8 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

cs.LG2020★ 2 cited

Defending against substitute model black box adversarial attacks with the 01 loss

Yunzhe Xue, Meiyan Xie, Usman Roshan

Substitute model black box attacks can create adversarial examples for a target model just by accessing its output labels. This poses a major challenge to machine learning models i…

cs.LG2020

Towards adversarial robustness with 01 loss neural networks

Yunzhe Xue, Meiyan Xie, Usman Roshan

Motivated by the general robustness properties of the 01 loss we propose a single hidden layer 01 loss neural network trained with stochastic coordinate descent as a defense agains…

cs.LG2020★ 1 cited

On the transferability of adversarial examples between convex and 01 loss models

Yunzhe Xue, Meiyan Xie, Usman Roshan

The 01 loss gives different and more accurate boundaries than convex loss models in the presence of outliers. Could the difference of boundaries translate to adversarial examples t…

cs.LG2020★ 1 cited

Robust binary classification with the 01 loss

Yunzhe Xue, Meiyan Xie, Usman Roshan

The 01 loss is robust to outliers and tolerant to noisy data compared to convex loss functions. We conjecture that the 01 loss may also be more robust to adversarial attacks. To st…

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