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📚TheoryIntermediate

Message Passing on Meshes & Point Clouds

Message passing treats meshes and point clouds as graphs where nodes exchange information with neighbors to learn useful features.

#geometric deep learning#message passing#pointnet+12
📚TheoryAdvanced

E(n) Equivariant Neural Networks

E(n)-equivariant neural networks are models whose outputs transform predictably when inputs are rotated, translated, or reflected in n-dimensional Euclidean space.

#e(n)-equivariance
Advanced
Filtering by:
#message passing
#euclidean group
#so(n) and o(n)
+12
📚TheoryAdvanced

Gauge Equivariant Networks

Gauge equivariant networks are neural networks that respect local symmetries (gauges) on manifolds, such as how vectors rotate when you change the local reference frame on a surface.

#gauge equivariant networks#geometric deep learning#manifold learning+12
📚TheoryIntermediate

Self-Attention as Graph Neural Network

Self-attention can be viewed as message passing on a fully connected graph where each token (node) sends a weighted message to every other token.

#self-attention#graph neural network#message passing+11
📚TheoryAdvanced

Graph Neural Network Theory

Graph Neural Networks (GNNs) learn on graphs by repeatedly letting each node aggregate messages from its neighbors and update its representation.

#graph neural networks#message passing#weisfeiler-leman+12
📚TheoryAdvanced

Distributed Algorithm Theory

Distributed algorithm theory studies how many independent computers cooperate correctly and efficiently despite delays and failures.

#distributed algorithms#message passing#synchronous rounds+12