Все публикации

1:26:00

Lecture 6: Gauge-equivariant Mesh CNN - Pim de Haan

1:28:35

Lecture 2: Topological Message Passing - Cristian Bodnar

1:28:14

Lecture 8: Curvature & Oversquashing in GNNs - Francesco Di Giovanni

1:28:03

Lecture 7: From Equivariance to Naturality - Pim de Haan

0:42:07

Prerequisites III: Manifolds & Fiber Bundles - Maurice Weiler

1:30:03

Lecture 10: What's Next? - Michael Bronstein

1:34:14

Lecture 1: A Brief History of Geometric Deep Learning - Michael Bronstein

1:10:50

Prerequisites I: Groups, Representations & Equivariance - Maurice Weiler

1:31:51

Lecture 4: Equivariant CNNs I (Euclidean Spaces) - Maurice Weiler

0:22:57

Prerequisites II: Topology - Cristian Bodnar

1:27:20

Lecture 5: Equivariant CNNs II (Riemannian manifolds) - Maurice Weiler

1:28:36

Lecture 3: Sheaf Neural Networks - Cristian Bodnar

1:34:42

Lecture 9: GNNs as Dynamic Systems - Francesco Di Giovanni

0:21:10

Prerequisites IV: Category Theory - Pim de Haan

1:00:24

AMMI 2022 Course 'Geometric Deep Learning' - Seminar 2 (Subgraph GNNs) - Fabrizio Frasca

1:01:37

AMMI 2022 Course 'Geometric Deep Learning' - Lecture 5 (Graphs & Sets) - Petar Veličković

0:46:54

AMMI 2022 Course 'Geometric Deep Learning' - Lecture 10 (Gauges) - Taco Cohen

1:05:21

AMMI 2022 Course 'Geometric Deep Learning' - Lecture 7 (Grids) - Joan Bruna

1:12:39

AMMI 2022 Course 'Geometric Deep Learning' - Seminar 1 (Physics-based GNNs) - Francesco Di Giovanni

0:56:48

AMMI 2022 Course 'Geometric Deep Learning' - Lecture 3 (Geometric Priors I) - Taco Cohen

1:19:40

AMMI 2022 Course 'Geometric Deep Learning' - Lecture 2 (Learning in High Dimensions) - Joan Bruna

1:11:00

AMMI 2022 Course 'Geometric Deep Learning' - Lecture 12 (Applications & Trends) - Michael Bronstein

1:15:33

AMMI 2022 Course 'Geometric Deep Learning' - Lecture 11 (Beyond Groups) - Petar Veličković

1:11:51

AMMI 2022 Course 'Geometric Deep Learning' - Lecture 8 (Groups & Homogeneous spaces) - Taco Cohen