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researcher

Yash Sharma

24 papers hereh-index 206.9k citations29 works total

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

author position
  • sole author1
  • first author5
  • middle author16
  • last author1

Across the 23 of 24 papers where every author was matched, so the position is known.

fields
  • cs.LG9
  • stat.ML7
  • cs.CV4
  • cs.CL1
  • cs.RO1
  • q-fin.TR1
Homepage
same name
  • Yash Sharma — 5 papers, h 5
  • Yash Sharma — 4 papers, h 3
  • Yash Sharma — 2 papers, h 5
  • Yash Sharma — 1 paper, h 1
  • Yash Sharma — 1 paper, h 4
  • Yash Sharma — 1 paper, h 4

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
20162023
most citedZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models

1.8k citations · 2.6k across the 22 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2022★ 1 cited

Pixel-level Correspondence for Self-Supervised Learning from Video

Yash Sharma, Yi Zhu, Chris Russell +1

While self-supervised learning has enabled effective representation learning in the absence of labels, for vision, video remains a relatively untapped source of supervision. To add…

cs.CV2021★ 11 cited

Unsupervised Learning of Compositional Energy Concepts

Yilun Du, Shuang Li, Yash Sharma +2

Humans are able to rapidly understand scenes by utilizing concepts extracted from prior experience. Such concepts are diverse, and include global scene descriptors, such as the wea…

cs.CV2020★ 4 cited

Benchmarking Unsupervised Object Representations for Video Sequences

Marissa A. Weis, Kashyap Chitta, Yash Sharma +4

Perceiving the world in terms of objects and tracking them through time is a crucial prerequisite for reasoning and scene understanding. Recently, several methods have been propose…

cs.CV2019

On the Effectiveness of Low Frequency Perturbations

Yash Sharma, Gavin Weiguang Ding, Marcus Brubaker

Carefully crafted, often imperceptible, adversarial perturbations have been shown to cause state-of-the-art models to yield extremely inaccurate outputs, rendering them unsuitable…

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