Gradient estimation of probabilistic programs reduces soundly to probabilistic inference after programmable coupling and factorization, enabling new low-variance estimators that beat baselines.
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4 Pith papers cite this work, alongside 314 external citations. Polarity classification is still indexing.
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A large-scale benchmark of 17 WHAR models across 30 datasets finds predictive performance has plateaued while efficiency favors compact neural models and random forests on the Pareto frontier.
Mitigation strategy for exploding gradients at material boundaries in differentiable radiation transport enables stable, optimization-ready derivatives for detector design.
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GradInf: Gradient Estimation as Probabilistic Inference
Gradient estimation of probabilistic programs reduces soundly to probabilistic inference after programmable coupling and factorization, enabling new low-variance estimators that beat baselines.
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WHAR Arena: Benchmarking the State of the Art in Efficient Wearable Human Activity Recognition
A large-scale benchmark of 17 WHAR models across 30 datasets finds predictive performance has plateaued while efficiency favors compact neural models and random forests on the Pareto frontier.
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Exploring the Boundaries of Differentiable Radiation Transport and Detector Simulation
Mitigation strategy for exploding gradients at material boundaries in differentiable radiation transport enables stable, optimization-ready derivatives for detector design.
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