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Found 7 papers, 1 papers with code
Rethinking SO(3)-equivariance with Bilinear Tensor Networks
Many datasets in scientific and engineering applications are comprised of objects which have specific geometric structure.
Symmetry Group Equivariant Architectures for Physics
Physical theories grounded in mathematical symmetries are an essential component of our understanding of a wide range of properties of the universe.
Particle Convolution for High Energy Physics
We introduce the Particle Convolution Network (PCN), a new type of equivariant neural network layer suitable for many tasks in jet physics.
AI Safety for High Energy Physics
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.
Beyond Imitation: Generative and Variational Choreography via Machine Learning
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.
Muon Trigger for Mobile Phones
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
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.
