Deep sequence models develop geometric memory in embeddings that encodes novel global relationships, transforming l-fold composition tasks into 1-step navigation via a natural spectral bias connected to Node2Vec.
Graph Embedding Techniques, Applications, and Performance: A Survey.Knowledge- Based Systems
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NOMAD delivers an MPI-based distributed implementation of graph embedding models achieving 10-100x median speedups over multi-threaded baselines and 35-76x over prior distributed systems on large clusters.
Determines the exact wirelength of embedding 3-ary n-cubes into cylinders and certain trees.
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Deep sequence models tend to memorize geometrically; it is unclear why
Deep sequence models develop geometric memory in embeddings that encodes novel global relationships, transforming l-fold composition tasks into 1-step navigation via a natural spectral bias connected to Node2Vec.
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NOMAD: Generating Embeddings for Massive Distributed Graphs
NOMAD delivers an MPI-based distributed implementation of graph embedding models achieving 10-100x median speedups over multi-threaded baselines and 35-76x over prior distributed systems on large clusters.
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Exact Wirelength of Embedding 3-Ary n-Cubes into certain Cylinders and Trees
Determines the exact wirelength of embedding 3-ary n-cubes into cylinders and certain trees.