1개의 강의가 검색되었습니다.
교수자/개설자
-
학습기간
2024-03-01 ~ 2024-12-31
강좌소개
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
참여자수
44
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