Yash Sharma | DeepAI

AI Chat

Featured Co-authors

Yoshua Bengio
448 publications
Bernhard Schölkopf
271 publications
Pin-Yu Chen
183 publications
Joshua B. Tenenbaum
180 publications
Kai-Wei Chang
160 publications
Cho-Jui Hsieh
147 publications
Shuang Li
106 publications
Thomas Brox
103 publications
Yi Zhu
96 publications
Andreas Geiger
81 publications
Huan Zhang
73 publications

∙ 08/24/2023

The intersection of video capsule endoscopy and artificial intelligence: addressing unique challenges using machine learning

Introduction: Technical burdens and time-intensive review processes limi...

0 Shan Guleria, et al. ∙

∙ 05/26/2023

Demo2Code: From Summarizing Demonstrations to Synthesizing Code via Extended Chain-of-Thought

Language instructions and demonstrations are two natural ways for users ...

0 Huaxiaoyue Wang, et al. ∙

∙ 05/23/2023

Provably Learning Object-Centric Representations

Learning structured representations of the visual world in terms of obje...

0 Jack Brady, et al. ∙

∙ 08/07/2022

How Adversarial Robustness Transfers from Pre-training to Downstream Tasks

Given the rise of large-scale training regimes, adapting pre-trained mod...

0 Laura Fee Nern, et al. ∙

∙ 07/29/2022

Weakly Supervised Deep Instance Nuclei Detection using Points Annotation in 3D Cardiovascular Immunofluorescent Images

Two major causes of death in the United States and worldwide are stroke ...

18 Nazanin Moradinasab, et al. ∙

∙ 07/08/2022

Pixel-level Correspondence for Self-Supervised Learning from Video

While self-supervised learning has enabled effective representation lear...

1 Yash Sharma, et al. ∙

∙ 06/29/2022

MaNi: Maximizing Mutual Information for Nuclei Cross-Domain Unsupervised Segmentation

In this work, we propose a mutual information (MI) based unsupervised do...

0 Yash Sharma, et al. ∙

∙ 04/26/2022

Encoding Cardiopulmonary Exercise Testing Time Series as Images for Classification using Convolutional Neural Network

Exercise testing has been available for more than a half-century and is ...

0 Yash Sharma, et al. ∙

∙ 11/04/2021

Unsupervised Learning of Compositional Energy Concepts

Humans are able to rapidly understand scenes by utilizing concepts extra...

5 Yilun Du, et al. ∙

∙ 06/13/2021

HistoTransfer: Understanding Transfer Learning for Histopathology

Advancement in digital pathology and artificial intelligence has enabled...

0 Yash Sharma, et al. ∙

∙ 06/08/2021

Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style

Self-supervised representation learning has shown remarkable success in ...

11 Julius von Kügelgen, et al. ∙

∙ 03/19/2021

Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification

In recent years, the availability of digitized Whole Slide Images (WSIs)...

0 Yash Sharma, et al. ∙

∙ 02/17/2021

Contrastive Learning Inverts the Data Generating Process

Contrastive learning has recently seen tremendous success in self-superv...

8 Roland S. Zimmermann, et al. ∙

∙ 10/12/2020

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

Building Automatic Speech Recognition (ASR) systems for code-switched sp...

0 Yash Sharma, et al. ∙

∙ 07/21/2020

Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding

We construct an unsupervised learning model that achieves nonlinear dise...

5 David Klindt, et al. ∙

∙ 07/13/2020

S2RMs: Spatially Structured Recurrent Modules

Capturing the structure of a data-generating process by means of appropr...

5 Nasim Rahaman, et al. ∙

∙ 06/12/2020

Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences

Perceiving the world in terms of objects is a crucial prerequisite for r...

14 Marissa A. Weis, et al. ∙

∙ 05/23/2020

Devising Malware Characterstics using Transformers

With the increasing number of cybersecurity threats, it becomes more dif...

0 Simra Shahid, et al. ∙

∙ 02/28/2019

On the Effectiveness of Low Frequency Perturbations

Carefully crafted, often imperceptible, adversarial perturbations have b...

6 Yash Sharma, et al. ∙

∙ 12/06/2018

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

We propose Max-Margin Adversarial (MMA) training for directly maximizing...

0 Gavin Weiguang Ding, et al. ∙

∙ 09/29/2018

CAAD 2018: Generating Transferable Adversarial Examples

Deep neural networks (DNNs) are vulnerable to adversarial examples, pert...

0 Yash Sharma, et al. ∙

∙ 05/28/2018

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

Deep neural networks (DNNs) are vulnerable to adversarial examples, even...

0 Moustafa Alzantot, et al. ∙

∙ 04/21/2018

Generating Natural Language Adversarial Examples

Deep neural networks (DNNs) are vulnerable to adversarial examples, pert...

0 Moustafa Alzantot, et al. ∙

∙ 03/27/2018

Bypassing Feature Squeezing by Increasing Adversary Strength

Feature Squeezing is a recently proposed defense method which reduces th...

0 Yash Sharma, et al. ∙

∙ 10/30/2017

Attacking the Madry Defense Model with L_1-based Adversarial Examples

The Madry Lab recently hosted a competition designed to test the robustn...

0 Yash Sharma, et al. ∙

∙ 09/13/2017

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

Recent studies have highlighted the vulnerability of deep neural network...

0 Pin-Yu Chen, et al. ∙

∙ 08/14/2017

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...

0 Pin-Yu Chen, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world