Search Results - "Turaga, Pavan"

Reconstruction-Free Action Inference from Compressive Imagers by Kulkarni, Kuldeep, Turaga, Pavan

“…Persistent surveillance from camera networks, such as at parking lots, UAVs, etc., often results in large amounts of video data, resulting in significant…”
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ReconNet: Non-Iterative Reconstruction of Images from Compressively Sensed Measurements by Kulkarni, Kuldeep, Lohit, Suhas, Turaga, Pavan, Kerviche, Ronan, Ashok, Amit

“…The goal of this paper is to present a non-iterative and more importantly an extremely fast algorithm to reconstruct images from compressively sensed (CS)…”
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Digital medicine and the curse of dimensionality by Berisha, Visar, Krantsevich, Chelsea, Hahn, P. Richard, Hahn, Shira, Dasarathy, Gautam, Turaga, Pavan, Liss, Julie

“…Digital health data are multimodal and high-dimensional. A patient’s health state can be characterized by a multitude of signals including medical imaging,…”
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Training calibration-based counterfactual explainers for deep learning models in medical image analysis by Thiagarajan, Jayaraman J., Thopalli, Kowshik, Rajan, Deepta, Turaga, Pavan

“…The rapid adoption of artificial intelligence methods in healthcare is coupled with the critical need for techniques to rigorously introspect models and…”
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Convolutional Neural Networks for Noniterative Reconstruction of Compressively Sensed Images by Lohit, Suhas, Kulkarni, Kuldeep, Kerviche, Ronan, Turaga, Pavan, Ashok, Amit

“…Traditional algorithms for compressive sensing recovery are computationally expensive and are ineffective at low measurement rates. In this paper, we propose a…”
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Elastic functional coding of human actions: From vector-fields to latent variables by Anirudh, Rushil, Turaga, Pavan, Jingyong Su, Srivastava, Anuj

“…Human activities observed from visual sensors often give rise to a sequence of smoothly varying features. In many cases, the space of features can be formally…”
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Persistent homology of attractors for action recognition by Venkataraman, Vinay, Ramamurthy, Karthikeyan Natesan, Turaga, Pavan

“…In this paper, we propose a novel framework for dynamical analysis of human actions from 3D motion capture data using topological data analysis. We model human…”
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Functional data analysis approach for mapping change in time series: A case study using bicycle ridership patterns by Roy, Avipsa, Nelson, Trisalyn, Turaga, Pavan

“…•The manuscript demonstrates a novel framework for change detection in bicycle trips collected from Strava fitness app.•Temporal alignment approach is proposed…”
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The Surprising Effectiveness of Deep Orthogonal Procrustes Alignment in Unsupervised Domain Adaptation by Thopalli, Kowshik, Anirudh, Rushil, Turaga, Pavan, Thiagarajan, Jayaraman J.

“…Unsupervised domain adaptation (UDA) aims to transfer and adapt knowledge from a labeled source domain to an unlabeled target domain. Traditionally,…”
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Automated Movement Assessment in Stroke Rehabilitation by Ahmed, Tamim, Thopalli, Kowshik, Rikakis, Thanassis, Turaga, Pavan, Kelliher, Aisling, Huang, Jia-Bin, Wolf, Steven L.

“…We are developing a system for long term Semi-Automated Rehabilitation At the Home (SARAH) that relies on low-cost and unobtrusive video-based sensing. We…”
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Orthogonality and graph divergence losses promote disentanglement in generative models by Shukla, Ankita, Dadhich, Rishi, Singh, Rajhans, Rayas, Anirudh, Saidi, Pouria, Dasarathy, Gautam, Berisha, Visar, Turaga, Pavan

“…Over the last decade, deep generative models have evolved to generate realistic and sharp images. The success of these models is often attributed to an…”
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An Optical Flow-Based Approach for Minimally Divergent Velocimetry Data Interpolation by Turaga, Pavan, Nair, Priya, Das, Dhritiman, Kanberoglu, Berkay, Frakes, David

“…Three-dimensional (3D) biomedical image sets are often acquired with in-plane pixel spacings that are far less than the out-of-plane spacings between images…”
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Shape Distributions of Nonlinear Dynamical Systems for Video-Based Inference by Venkataraman, Vinay, Turaga, Pavan

“…This paper presents a shape-theoretic framework for dynamical analysis of nonlinear dynamical systems which appear frequently in several video-based inference…”
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RNAS-CL: Robust Neural Architecture Search by Cross-Layer Knowledge Distillation by Nath, Utkarsh, Wang, Yancheng, Turaga, Pavan, Yang, Yingzhen

“…Deep Neural Networks are often vulnerable to adversarial attacks. Neural Architecture Search (NAS), one of the tools for developing novel deep neural…”
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Geometry-Based Symbolic Approximation for Fast Sequence Matching on Manifolds by Anirudh, Rushil, Turaga, Pavan

“…In this paper, we consider the problem of fast and efficient indexing techniques for sequences evolving in non-Euclidean spaces. This problem has several…”
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Direct inference on compressive measurements using convolutional neural networks by Lohit, Suhas, Kulkarni, Kuldeep, Turaga, Pavan

“…Compressive imagers, e.g. the single-pixel camera (SPC), acquire measurements in the form of random projections of the scene instead of pixel intensities…”
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Compressive Light Field Reconstructions Using Deep Learning by Gupta, Mayank, Jauhari, Arjun, Kulkarni, Kuldeep, Jayasuriya, Suren, Molnar, Alyosha, Turaga, Pavan

“…Light field imaging is limited in its computational processing demands of high sampling for both spatial and angular dimensions. Single-shot light field…”
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Leveraging angular distributions for improved knowledge distillation by Jeon, Eun Som, Choi, Hongjun, Shukla, Ankita, Turaga, Pavan

“…Knowledge distillation as a broad class of methods has led to the development of lightweight and memory efficient models, using a pre-trained model with a…”
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Temporal Transformer Networks: Joint Learning of Invariant and Discriminative Time Warping by Lohit, Suhas, Wang, Qiao, Turaga, Pavan

“…Many time-series classification problems involve developing metrics that are invariant to temporal misalignment. In human activity analysis, temporal…”
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Modeling and Visualization of Human Activities for Multicamera Networks by Sankaranarayanan, Aswin C., Patro, Robert, Turaga, Pavan, Varshney, Amitabh, Chellappa, Rama

“…Multicamera networks are becoming complex involving larger sensing areas in order to capture activities and behavior that evolve over long spatial and temporal…”
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