Výsledky vyhledávání - "Joshi, Shantanu H"

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Differentiable VQ-VAE's for Robust White Matter Streamline Encodings

Autor: Lizarraga, Andrew, Taraku, Brandon, Honig, Edouardo, Wu, Ying Nian, Joshi, Shantanu H.

Given the complex geometry of white matter streamlines, Autoencoders have been proposed as a dimension-reduction tool to simplify the analysis streamlines in a low-dimensional latent spaces. However, despite these recent successes, the majority of en

Externí odkaz: http://arxiv.org/abs/2311.06212

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StreamNet: A WAE for White Matter Streamline Analysis

Autor: Lizarraga, Andrew, Narr, Katherine L., Donald, Kirsten A., Joshi, Shantanu H.

We present StreamNet, an autoencoder architecture for the analysis of the highly heterogeneous geometry of large collections of white matter streamlines. This proposed framework takes advantage of geometry-preserving properties of the Wasserstein-1 m

Externí odkaz: http://arxiv.org/abs/2209.01498

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Alignment of Tractography Streamlines using Deformation Transfer via Parallel Transport

Autor: Lizarraga, Andrew, Lee, David, Kubicki, Antoni, Sahib, Ashish, Nunez, Elvis, Narr, Katherine, Joshi, Shantanu H.

We present a geometric framework for aligning white matter fiber tracts. By registering fiber tracts between brains, one expects to see overlap of anatomical structures that often provide meaningful comparisons across subjects. However, the geometry

Externí odkaz: http://arxiv.org/abs/2108.03697

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SrvfNet: A Generative Network for Unsupervised Multiple Diffeomorphic Shape Alignment

Autor: Nunez, Elvis, Lizarraga, Andrew, Joshi, Shantanu H.

We present SrvfNet, a generative deep learning framework for the joint multiple alignment of large collections of functional data comprising square-root velocity functions (SRVF) to their templates. Our proposed framework is fully unsupervised and is

Externí odkaz: http://arxiv.org/abs/2104.13449

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Automating Artifact Detection in Video Games

Autor: Davarmanesh, Parmida, Jiang, Kuanhao, Ou, Tingting, Vysogorets, Artem, Ivashkevich, Stanislav, Kiehn, Max, Joshi, Shantanu H., Malaya, Nicholas

In spite of advances in gaming hardware and software, gameplay is often tainted with graphics errors, glitches, and screen artifacts. This proof of concept study presents a machine learning approach for automated detection of graphics corruptions in

Externí odkaz: http://arxiv.org/abs/2011.15103

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