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cs.LG2026
Supercharging Simulation-Based Inference for Bayesian Optimal Experimental Design
Samuel Klein, Willie Neiswanger, Daniel Ratner +2
Bayesian optimal experimental design (BOED) seeks to maximize the expected information gain (EIG) of experiments. This requires a likelihood estimate, which in many settings is int…
cs.LG2026
AIE4ML: An End-to-End Framework for Compiling Neural Networks for the Next Generation of AMD AI Engines
Dimitrios Danopoulos, Enrico Lupi, Chang Sun +4
Efficient AI inference on AMD's Versal AI Engine (AIE) is challenging due to tightly coupled VLIW execution, explicit datapaths, and local memory management. Prior work focused on…