Introduces statistically efficient estimators for Renyi-α, Tsallis-α, reverse and forward KL divergences with REINFORCE and score-matching control variates for faster GFlowNet training.
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Multimodal neural operators predict full-field brain displacement from MRE data with high accuracy and fast inference by fusing volumetric imaging, demographics, and acquisition parameters.
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On Divergence Measures for Training GFlowNets
Introduces statistically efficient estimators for Renyi-α, Tsallis-α, reverse and forward KL divergences with REINFORCE and score-matching control variates for faster GFlowNet training.
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Multimodal Neural Operators for Real-Time Biomechanical Modelling of Traumatic Brain Injury
Multimodal neural operators predict full-field brain displacement from MRE data with high accuracy and fast inference by fusing volumetric imaging, demographics, and acquisition parameters.