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Siddhant Garg

University of Wisconsin Madison, IIT Bombay, Amazon Alexa AI

20 papers hereh-index 111.3k citations37 works total

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

author position
  • sole author1
  • first author10
  • middle author7
  • last author2

Across the 20 of 20 papers where every author was matched, so the position is known.

fields
  • cs.CL9
  • cs.LG4
  • cs.CV2
  • q-bio.GN2
  • cs.AI1
  • cs.AR1
affiliations
  • University of Wisconsin Madison, IIT Bombay, Amazon Alexa AI
Homepage
same name
  • Siddhant Garg — 2 papers
  • Siddhant Garg — 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
20192026
most citedCan Adversarial Weight Perturbations Inject Neural Backdoors?

61 citations · 113 across the 16 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2023★ 1 cited

Structured Pruning for Multi-Task Deep Neural Networks

Siddhant Garg, Lijun Zhang, Hui Guan

Although multi-task deep neural network (DNN) models have computation and storage benefits over individual single-task DNN models, they can be further optimized via model compressi…

cs.LG2020★ 61 cited

Can Adversarial Weight Perturbations Inject Neural Backdoors?

Siddhant Garg, Adarsh Kumar, Vibhor Goel +1

Adversarial machine learning has exposed several security hazards of neural models and has become an important research topic in recent times. Thus far, the concept of an "adversar…

cs.LG2020

Functional Regularization for Representation Learning: A Unified Theoretical Perspective

Siddhant Garg, Yingyu Liang

Unsupervised and self-supervised learning approaches have become a crucial tool to learn representations for downstream prediction tasks. While these approaches are widely used in…

cs.LG2019★ 3 cited

Stochastic Bandits with Delayed Composite Anonymous Feedback

Siddhant Garg, Aditya Kumar Akash

We explore a novel setting of the Multi-Armed Bandit (MAB) problem inspired from real world applications which we call bandits with "stochastic delayed composite anonymous feedback…

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