Rushil Anirudh

DeepAI

Featured Co-authors

Yi Zhou
114 publications
Di Wang
102 publications
Yingbin Liang
69 publications
Jayaraman J. Thiagarajan
44 publications
Karthikeyan Natesan Ramamurthy
44 publications
Pavan Turaga
34 publications
Peter Robinson
33 publications
Anuj Srivastava
25 publications
Bhavya Kailkhura
23 publications
Peer-Timo Bremer
19 publications
Valerio Pascucci
15 publications

research

∙ 01/05/2022

Revisiting Deep Subspace Alignment for Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) aims to transfer and adapt knowledg...

Kowshik Thopalli, et al. ∙

research

∙ 12/03/2020

Recovering Trajectories of Unmarked Joints in 3D Human Actions Using Latent Space Optimization

Motion capture (mocap) and time-of-flight based sensing of human actions...

Suhas Lohit, et al. ∙

research

∙ 06/18/2020

Generative Patch Priors for Practical Compressive Image Recovery

In this paper, we propose the generative patch prior (GPP) that defines ...

Rushil Anirudh, et al. ∙

research

∙ 05/05/2020

Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models

Predictive models that accurately emulate complex scientific processes c...

Jayaraman J. Thiagarajan, et al. ∙

research

∙ 12/17/2019

Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies

Neural networks have become very popular in surrogate modeling because o...

Rushil Anirudh, et al. ∙

research

∙ 12/16/2019

MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking

In the past few years, generative models like Generative Adversarial Net...

Rushil Anirudh, et al. ∙

research

∙ 12/05/2019

Merlin: Enabling Machine Learning-Ready HPC Ensembles

With the growing complexity of computational and experimental facilities...

J. Luc Peterson, et al. ∙

research

∙ 10/11/2019

Extreme Few-view CT Reconstruction using Deep Inference

Reconstruction of few-view x-ray Computed Tomography (CT) data is a high...

Hyojin Kim, et al. ∙

research

∙ 10/05/2019

Parallelizing Training of Deep Generative Models on Massive Scientific Datasets

Training deep neural networks on large scientific data is a challenging ...

Sam Ade Jacobs, et al. ∙

research

∙ 10/03/2019

Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion

There is significant interest in using modern neural networks for scient...

Rushil Anirudh, et al. ∙

research

∙ 10/03/2019

Improving Limited Angle CT Reconstruction with a Robust GAN Prior

Limited angle CT reconstruction is an under-determined linear inverse pr...

Rushil Anirudh, et al. ∙

research

∙ 09/25/2019

Function Preserving Projection for Scalable Exploration of High-Dimensional Data

We present function preserving projections (FPP), a scalable linear proj...

Shusen Liu, et al. ∙

research

∙ 07/19/2019

Scalable Topological Data Analysis and Visualization for Evaluating Data-Driven Models in Scientific Applications

With the rapid adoption of machine learning techniques for large-scale a...

Shusen Liu, et al. ∙

research

∙ 06/11/2019

SALT: Subspace Alignment as an Auxiliary Learning Task for Domain Adaptation

Unsupervised domain adaptation aims to transfer and adapt knowledge lear...

Kowshik Thopalli, et al. ∙

research

∙ 11/22/2018

MR-GAN: Manifold Regularized Generative Adversarial Networks

Despite the growing interest in generative adversarial networks (GANs), ...

Qunwei Li, et al. ∙

research

∙ 11/20/2018

MimicGAN: Corruption-Mimicking for Blind Image Recovery & Adversarial Defense

Solving inverse problems continues to be a central challenge in computer...

Rushil Anirudh, et al. ∙

research

∙ 11/11/2018

Multiple Subspace Alignment Improves Domain Adaptation

We present a novel unsupervised domain adaptation (DA) method for cross-...

Kowshik Thopalli, et al. ∙

research

∙ 10/31/2018

Unsupervised Dimension Selection using a Blue Noise Spectrum

Unsupervised dimension selection is an important problem that seeks to r...

Jayaraman J. Thiagarajan, et al. ∙

research

∙ 10/31/2018

Understanding Deep Neural Networks through Input Uncertainties

Techniques for understanding the functioning of complex machine learning...

Jayaraman J. Thiagarajan, et al. ∙

research

∙ 05/18/2018

An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks

Solving inverse problems continues to be a challenge in a wide array of ...

Rushil Anirudh, et al. ∙

research

∙ 11/28/2017

Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion

Computed Tomography (CT) reconstruction is a fundamental component to a ...

Rushil Anirudh, et al. ∙

research

∙ 11/15/2017

Influential Sample Selection: A Graph Signal Processing Approach

With the growing complexity of machine learning techniques, understandin...

Rushil Anirudh, et al. ∙

research

∙ 04/24/2017

Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification

Using predictive models to identify patterns that can act as biomarkers ...

Rushil Anirudh, et al. ∙

research

∙ 10/29/2016

Diversity Promoting Online Sampling for Streaming Video Summarization

Many applications benefit from sampling algorithms where a small number ...

Rushil Anirudh, et al. ∙

research

∙ 05/28/2016

A Riemannian Framework for Statistical Analysis of Topological Persistence Diagrams

Topological data analysis is becoming a popular way to study high dimens...

Rushil Anirudh, et al. ∙

research

∙ 03/07/2016

Elastic Functional Coding of Riemannian Trajectories

Visual observations of dynamic phenomena, such as human actions, are oft...

Rushil Anirudh, et al. ∙

research

∙ 06/21/2014

Interactively Test Driving an Object Detector: Estimating Performance on Unlabeled Data

In this paper, we study the problem of `test-driving' a detector, i.e. a...

Rushil Anirudh, et al. ∙