TRIE benchmarks stochastic PDE surrogates on two chaotic SPDEs, finding generative models best match long-term statistics and uncertainty while latent versions cut inference time by 12x.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
A hyperprior on the effective output variance of deep ReLU Bayesian neural networks yields simultaneously admissible and minimax decision rules in the normal location model under quadratic loss.
GRAPE augments prototype medical image classifiers with graph attention for co-occurrence, a mismatch safety check, and open-vocabulary anchoring to support incremental addition of findings from single examples.
citing papers explorer
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TRIE: An Evaluation Framework for Stochastic PDE Surrogates
TRIE benchmarks stochastic PDE surrogates on two chaotic SPDEs, finding generative models best match long-term statistics and uncertainty while latent versions cut inference time by 12x.
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Minimaxity and Admissibility of Bayesian Neural Networks
A hyperprior on the effective output variance of deep ReLU Bayesian neural networks yields simultaneously admissible and minimax decision rules in the normal location model under quadratic loss.
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GRAPE: Graph-Augmented Prototype Explanations for Interactive Medical Image Diagnosis
GRAPE augments prototype medical image classifiers with graph attention for co-occurrence, a mismatch safety check, and open-vocabulary anchoring to support incremental addition of findings from single examples.