e3nn: Euclidean Neural Networks | Mario Geiger скачать в хорошем качестве

e3nn: Euclidean Neural Networks | Mario Geiger

Join the Learning on Graphs and Geometry Reading Group: https://hannes-stark.com/logag-readin... Paper “e3nn: Euclidean Neural Networks": https://arxiv.org/abs/2207.09453 Abstract: We present e3nn, a generalized framework for creating E(3) equivariant trainable functions, also known as Euclidean neural networks. e3nn naturally operates on geometry and geometric tensors that describe systems in 3D and transform predictably under a change of coordinate system. The core of e3nn are equivariant operations such as the TensorProduct class or the spherical harmonics functions that can be composed to create more complex modules such as convolutions and attention mechanisms. These core operations of e3nn can be used to efficiently articulate Tensor Field Networks, 3D Steerable CNNs, Clebsch-Gordan Networks, SE(3) Transformers and other E(3) equivariant networks. Authors: Mario Geiger, Tess Smidt Twitter Hannes:   / hannesstaerk   Twitter Dominique:   / dom_beaini   Twitter Valence Discovery:   / valence_ai   Reading Group Slack: https://join.slack.com/t/logag/shared... ~ Chapters 00:00 - Intro 03:03 - What is e3nn? 03:57 - Group representations 37:15 - Spherical harmonics 48:36 - Tensor product