A mean-pool deep set trained on sets of size at most two produces an encoder that generalizes to arbitrary sizes, decoupling representation learning from posterior modeling and making training cost independent of deployment set size N.
IEEE transactions on neural networks and learning systems , volume=
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Introduces the adaptive_ts package and tutorial for trajectory-oriented optimization of stochastic simulators via adaptive Thompson sampling and grid refinement.
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It Just Takes Two: Scaling Amortized Inference to Large Sets
A mean-pool deep set trained on sets of size at most two produces an encoder that generalizes to arbitrary sizes, decoupling representation learning from posterior modeling and making training cost independent of deployment set size N.
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Trajectory-Oriented Optimization Via Adaptive Thompson Sampling And Grid Refinement: A Tutorial With The ADAPTIVE\_TS Package
Introduces the adaptive_ts package and tutorial for trajectory-oriented optimization of stochastic simulators via adaptive Thompson sampling and grid refinement.