S. J. Thais

1. Applications and Techniques for Fast Machine Learning in Science
   2021/10/25 by Allison McCarn Deiana, A. M. Deiana, Nhan Viet Tran +111 · 1 voice · 5 citations
   Decision Sciences · Computer Science · #Scientific Computing and Data Management #Neural Networks and Reservoir Computing #Anomaly Detection Techniques and Applications
2. Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges
   2022/03/23 by S. J. Thais, Thais, Savannah, P. Calafiura +17 · 6 citations
   Computer Science · Materials Science · #Advanced Graph Neural Networks #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #High Energy Physics - Phenomenology (hep-ph) #Machine Learning (cs.LG) #Machine Learning in Materials Science #Parallel Computing and Optimization Techniques
3. Symmetry Group Equivariant Architectures for Physics
   2022/03/11 by Alexander Bogatskiy, S. Ganguly, Bogatskiy, Alexander +19 · 5 citations
   Chemistry · Materials Science · Physics and Astronomy · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #High Energy Physics - Phenomenology (hep-ph) #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Machine Learning (cs.LG) #Machine Learning in Materials Science #Molecular spectroscopy and chirality #Quantum many-body systems
4. Equivariance Is Not All You Need: Characterizing the Utility of Equivariant Graph Neural Networks for Particle Physics Tasks
   2023/11/06 by S. J. Thais, Thais, Savannah, Daniel Murnane +1 · 1 citation
   Computer Science · Materials Science · Physics and Astronomy · #Computational Physics and Python Applications #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Machine Learning (cs.LG) #Machine Learning in Materials Science #Quantum many-body systems