
Recovering Trajectories of Unmarked Joints in 3D Human Actions Using Latent Space Optimization
Motion capture (mocap) and timeofflight based sensing of human actions...
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Generative Patch Priors for Practical Compressive Image Recovery
In this paper, we propose the generative patch prior (GPP) that defines ...
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Designing Accurate Emulators for Scientific Processes using CalibrationDriven Deep Models
Predictive models that accurately emulate complex scientific processes c...
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Improved Surrogates in Inertial Confinement Fusion with Manifold and Cycle Consistencies
Neural networks have become very popular in surrogate modeling because o...
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MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking
In the past few years, generative models like Generative Adversarial Net...
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Merlin: Enabling Machine LearningReady HPC Ensembles
With the growing complexity of computational and experimental facilities...
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Extreme Fewview CT Reconstruction using Deep Inference
Reconstruction of fewview xray Computed Tomography (CT) data is a high...
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Parallelizing Training of Deep Generative Models on Massive Scientific Datasets
Training deep neural networks on large scientific data is a challenging ...
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Exploring Generative Physics Models with Scientific Priors in Inertial Confinement Fusion
There is significant interest in using modern neural networks for scient...
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Improving Limited Angle CT Reconstruction with a Robust GAN Prior
Limited angle CT reconstruction is an underdetermined linear inverse pr...
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Function Preserving Projection for Scalable Exploration of HighDimensional Data
We present function preserving projections (FPP), a scalable linear proj...
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Scalable Topological Data Analysis and Visualization for Evaluating DataDriven Models in Scientific Applications
With the rapid adoption of machine learning techniques for largescale a...
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SALT: Subspace Alignment as an Auxiliary Learning Task for Domain Adaptation
Unsupervised domain adaptation aims to transfer and adapt knowledge lear...
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MRGAN: Manifold Regularized Generative Adversarial Networks
Despite the growing interest in generative adversarial networks (GANs), ...
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MimicGAN: CorruptionMimicking for Blind Image Recovery & Adversarial Defense
Solving inverse problems continues to be a central challenge in computer...
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Multiple Subspace Alignment Improves Domain Adaptation
We present a novel unsupervised domain adaptation (DA) method for cross...
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Unsupervised Dimension Selection using a Blue Noise Spectrum
Unsupervised dimension selection is an important problem that seeks to r...
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Understanding Deep Neural Networks through Input Uncertainties
Techniques for understanding the functioning of complex machine learning...
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An Unsupervised Approach to Solving Inverse Problems using Generative Adversarial Networks
Solving inverse problems continues to be a challenge in a wide array of ...
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Lose The Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion
Computed Tomography (CT) reconstruction is a fundamental component to a ...
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Influential Sample Selection: A Graph Signal Processing Approach
With the growing complexity of machine learning techniques, understandin...
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Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification
Using predictive models to identify patterns that can act as biomarkers ...
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Diversity Promoting Online Sampling for Streaming Video Summarization
Many applications benefit from sampling algorithms where a small number ...
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A Riemannian Framework for Statistical Analysis of Topological Persistence Diagrams
Topological data analysis is becoming a popular way to study high dimens...
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Elastic Functional Coding of Riemannian Trajectories
Visual observations of dynamic phenomena, such as human actions, are oft...
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Interactively Test Driving an Object Detector: Estimating Performance on Unlabeled Data
In this paper, we study the problem of `testdriving' a detector, i.e. a...
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Rushil Anirudh
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