Search Results for author: Chase Shimmin

Found 8 papers, 1 papers with code

Calo-VQ: Vector-Quantized Two-Stage Generative Model in Calorimeter Simulation

no code implementations10 May 2024 Qibin Liu, Chase Shimmin, Xiulong Liu, Eli Shlizerman, Shu Li, Shih-Chieh Hsu

We introduce a novel machine learning method developed for the fast simulation of calorimeter detector response, adapting vector-quantized variational autoencoder (VQ-VAE).

Rethinking SO(3)-equivariance with Bilinear Tensor Networks

no code implementations20 Mar 2023 Chase Shimmin, Zhelun Li, Ema Smith

Many datasets in scientific and engineering applications are comprised of objects which have specific geometric structure.

Tensor Networks

Symmetry Group Equivariant Architectures for Physics

no code implementations11 Mar 2022 Alexander Bogatskiy, Sanmay Ganguly, Thomas Kipf, Risi Kondor, David W. Miller, Daniel Murnane, Jan T. Offermann, Mariel Pettee, Phiala Shanahan, Chase Shimmin, Savannah Thais

Physical theories grounded in mathematical symmetries are an essential component of our understanding of a wide range of properties of the universe.

BIG-bench Machine Learning

Particle Convolution for High Energy Physics

no code implementations5 Jul 2021 Chase Shimmin

We introduce the Particle Convolution Network (PCN), a new type of equivariant neural network layer suitable for many tasks in jet physics.

Vocal Bursts Intensity Prediction

AI Safety for High Energy Physics

no code implementations18 Oct 2019 Benjamin Nachman, Chase Shimmin

The field of high-energy physics (HEP), along with many scientific disciplines, is currently experiencing a dramatic influx of new methodologies powered by modern machine learning techniques.

Vocal Bursts Intensity Prediction

Beyond Imitation: Generative and Variational Choreography via Machine Learning

1 code implementation11 Jul 2019 Mariel Pettee, Chase Shimmin, Douglas Duhaime, Ilya Vidrin

Our team of dance artists, physicists, and machine learning researchers has collectively developed several original, configurable machine-learning tools to generate novel sequences of choreography as well as tunable variations on input choreographic sequences.

BIG-bench Machine Learning

Muon Trigger for Mobile Phones

no code implementations25 Sep 2017 Maxim Borisyak, Michail Usvyatsov, Michael Mulhearn, Chase Shimmin, Andrey Ustyuzhanin

The CRAYFIS experiment proposes to use privately owned mobile phones as a ground detector array for Ultra High Energy Cosmic Rays.

Decorrelated Jet Substructure Tagging using Adversarial Neural Networks

no code implementations10 Mar 2017 Chase Shimmin, Peter Sadowski, Pierre Baldi, Edison Weik, Daniel Whiteson, Edward Goul, Andreas Søgaard

We describe a strategy for constructing a neural network jet substructure tagger which powerfully discriminates boosted decay signals while remaining largely uncorrelated with the jet mass.

Jet Tagging

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