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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#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
Advanced
Filtering by:
#message passing
#geometric deep learning
#manifold learning
+12
📚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