Yash Sharma

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ibm ∙ Cooper Union

Improving Low Resource Code-switched ASR using Augmented Code-switched TTS

Building Automatic Speech Recognition (ASR) systems for code-switched sp...
10/12/2020 ∙ by Yash Sharma, et al. ∙ 0 ∙ share

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Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

We construct an unsupervised learning model that achieves nonlinear dise...
07/21/2020 ∙ by David Klindt, et al. ∙ 5 ∙ share

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S2RMs: Spatially Structured Recurrent Modules

Capturing the structure of a data-generating process by means of appropr...
07/13/2020 ∙ by Nasim Rahaman, et al. ∙ 5 ∙ share

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Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences

Perceiving the world in terms of objects is a crucial prerequisite for r...
06/12/2020 ∙ by Marissa A. Weis, et al. ∙ 14 ∙ share

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Devising Malware Characterstics using Transformers

With the increasing number of cybersecurity threats, it becomes more dif...
05/23/2020 ∙ by Simra Shahid, et al. ∙ 0 ∙ share

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On the Effectiveness of Low Frequency Perturbations

Carefully crafted, often imperceptible, adversarial perturbations have b...
02/28/2019 ∙ by Yash Sharma, et al. ∙ 6 ∙ share

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Max-Margin Adversarial (MMA) Training: Direct Input Space Margin Maximization through Adversarial Training

We propose Max-Margin Adversarial (MMA) training for directly maximizing...
12/06/2018 ∙ by Gavin Weiguang Ding, et al. ∙ 0 ∙ share

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CAAD 2018: Generating Transferable Adversarial Examples

Deep neural networks (DNNs) are vulnerable to adversarial examples, pert...
09/29/2018 ∙ by Yash Sharma, et al. ∙ 0 ∙ share

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GenAttack: Practical Black-box Attacks with Gradient-Free Optimization

Deep neural networks (DNNs) are vulnerable to adversarial examples, even...
05/28/2018 ∙ by Moustafa Alzantot, et al. ∙ 0 ∙ share

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Generating Natural Language Adversarial Examples

Deep neural networks (DNNs) are vulnerable to adversarial examples, pert...
04/21/2018 ∙ by Moustafa Alzantot, et al. ∙ 0 ∙ share

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Bypassing Feature Squeezing by Increasing Adversary Strength

Feature Squeezing is a recently proposed defense method which reduces th...
03/27/2018 ∙ by Yash Sharma, et al. ∙ 0 ∙ share

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Attacking the Madry Defense Model with L_1-based Adversarial Examples

The Madry Lab recently hosted a competition designed to test the robustn...
10/30/2017 ∙ by Yash Sharma, et al. ∙ 0 ∙ share

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EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

Recent studies have highlighted the vulnerability of deep neural network...
09/13/2017 ∙ by Pin-Yu Chen, et al. ∙ 0 ∙ share

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

Deep neural networks (DNNs) are one of the most prominent technologies o...
08/14/2017 ∙ by Pin-Yu Chen, et al. ∙ 0 ∙ share

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Yash Sharma

Featured Co-authors

Yoshua Bengio

Bernhard Schölkopf

Cho-Jui Hsieh

Pin-Yu Chen

Kai-Wei Chang

Matthias Bethge

Huan Zhang

Jinfeng Yi

Andreas Geiger

Stefan Bauer

Anirudh Goyal

Improving Low Resource Code-switched ASR using Augmented Code-switched TTS

Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

S2RMs: Spatially Structured Recurrent Modules

Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences

Devising Malware Characterstics using Transformers

On the Effectiveness of Low Frequency Perturbations

Max-Margin Adversarial (MMA) Training: Direct Input Space Margin Maximization through Adversarial Training

CAAD 2018: Generating Transferable Adversarial Examples

GenAttack: Practical Black-box Attacks with Gradient-Free Optimization

Generating Natural Language Adversarial Examples

Bypassing Feature Squeezing by Increasing Adversary Strength

Attacking the Madry Defense Model with L_1-based Adversarial Examples

EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples

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