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Found 4 papers, 1 papers with code
A Gated MLP Architecture for Learning Topological Dependencies in Spatio-Temporal Graphs
The Cy2Mixer is composed of three blocks based on MLPs: A message-passing block for encapsulating spatial information, a cycle message-passing block for enriching topological information through cyclic subgraphs, and a temporal block for capturing temporal properties.
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Provably Scalable Black-Box Variational Inference with Structured Variational Families
In fact, recent computational complexity results for BBVI have established that full-rank variational families scale poorly with the dimensionality of the problem compared to e. g. mean field families.
Modeling Choice via Self-Attention
Naturally, accurate estimation of these models from data is a critical step in the application of these optimization problems in practice.
Learning to Scale Logits for Temperature-Conditional GFlowNets
We find that the challenge is greatly reduced if a learned function of the temperature is used to scale the policy's logits directly.
