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20182023
most citedHarnessing the Vulnerability of Latent Layers in Adversarially Trained Models

23 citations · 54 across the 20 of their papers we have counts for

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cs.LG2023

SARC: Soft Actor Retrospective Critic

Sukriti Verma, Ayush Chopra, Jayakumar Subramanian +4

The two-time scale nature of SAC, which is an actor-critic algorithm, is characterised by the fact that the critic estimate has not converged for the actor at any given time, but s…

cs.LG2021

Form2Seq : A Framework for Higher-Order Form Structure Extraction

Milan Aggarwal, Hiresh Gupta, Mausoom Sarkar +1

Document structure extraction has been a widely researched area for decades with recent works performing it as a semantic segmentation task over document images using fully-convolu…

cs.LG2021

Information-theoretic Evolution of Model Agnostic Global Explanations

Sukriti Verma, Nikaash Puri, Piyush Gupta +1

Explaining the behavior of black box machine learning models through human interpretable rules is an important research area. Recent work has focused on explaining model behavior l…

cs.LG2020

ShapeVis: High-dimensional Data Visualization at Scale

Nupur Kumari, Siddarth R., Akash Rupela +2

We present ShapeVis, a scalable visualization technique for point cloud data inspired from topological data analysis. Our method captures the underlying geometric and topological s…

cs.LG20195 cited

A Method for Computing Class-wise Universal Adversarial Perturbations

Tejus Gupta, Abhishek Sinha, Nupur Kumari +2

We present an algorithm for computing class-specific universal adversarial perturbations for deep neural networks. Such perturbations can induce misclassification in a large fracti…

cs.LG2019

Charting the Right Manifold: Manifold Mixup for Few-shot Learning

Puneet Mangla, Mayank Singh, Abhishek Sinha +3

Few-shot learning algorithms aim to learn model parameters capable of adapting to unseen classes with the help of only a few labeled examples. A recent regularization technique - M…