1 Course(s)
Professor
-
Learning Period
03-01-2024 ~ 12-31-2024
Course Introduction
강의시간 강의내용 실습여부 1 - Why CNNs, MLPs, RNNs are insufficient for non-Euclidean data - Neural network for graphs & sets - Euclidean transformations, invariance, and equivariance 2 - Invariant geometric GNNs (SchNet, DimeNet, and SphereNet) - Simple equivariant geometric GNNs (EGNN and NequIP) O 3 - Local frame-based geometric GNNs (ClofNet and LEFTNet) - Frame averaging for geometric GNNs (Frame averaging, FAENet) O 4 - Steerable features, rreducible representations, Wigner-D matrix, spherical harmonics, Clebsch-Gordan tensor product - Steerable geometric GNNs (Tensor field network, SE(3)-Transformer) 5 - Steerable geometric GNNs (Equiformer, MACE, eSCN, EquifomerV2) O
Students
44
인공지능 & 프로그래밍|