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Unifying O(3) Equivariant Neural Networks Design with Tensor-Network Formalism
Zimu Li, Zihan Pengmei, Han Zheng +3
Many learning tasks, including learning potential energy surfaces from ab initio calculations, involve global spatial symmetries and permutational symmetry between atoms or general…
Predicting Drug-Drug Interactions using Deep Generative Models on Graphs
Nhat Khang Ngo, Truong Son Hy, Risi Kondor
Latent representations of drugs and their targets produced by contemporary graph autoencoder-based models have proved useful in predicting many types of node-pair interactions on l…
Toward Super-polynomial Quantum Speedup of Equivariant Quantum Algorithms with SU() Symmetry
Han Zheng, Zimu Li, Sergii Strelchuk +2
We introduce a framework of the equivariant convolutional quantum algorithms which is tailored for a number of machine-learning tasks on physical systems with arbitrary SU sym…
Temporal Multiresolution Graph Neural Networks For Epidemic Prediction
Truong Son Hy, Viet Bach Nguyen, Long Tran-Thanh +1
In this paper, we introduce Temporal Multiresolution Graph Neural Networks (TMGNN), the first architecture that both learns to construct the multiscale and multiresolution graph st…
Symmetry Group Equivariant Architectures for Physics
Alexander Bogatskiy, Sanmay Ganguly, Thomas Kipf +8
Physical theories grounded in mathematical symmetries are an essential component of our understanding of a wide range of properties of the universe. Similarly, in the domain of mac…