2 citations · 4 across the 8 of their papers we have counts for
4 papers · 1 filter
Diffusion models for probabilistic programming
Simon Dirmeier, Fernando Perez-Cruz
We propose Diffusion Model Variational Inference (DMVI), a novel method for automated approximate inference in probabilistic programming languages (PPLs). DMVI utilizes diffusion m…
A causal intervention framework for synthesizing mobility data and evaluating predictive neural networks
Ye Hong, Yanan Xin, Simon Dirmeier +2
Deep neural networks are increasingly utilized in mobility prediction tasks, yet their intricate internal workings pose challenges for interpretability, especially in comprehending…
Uncertainty quantification and out-of-distribution detection using surjective normalizing flows
Simon Dirmeier, Ye Hong, Yanan Xin +1
Reliable quantification of epistemic and aleatoric uncertainty is of crucial importance in applications where models are trained in one environment but applied to multiple differen…
Simulation-based Inference for High-dimensional Data using Surjective Sequential Neural Likelihood Estimation
Simon Dirmeier, Carlo Albert, Fernando Perez-Cruz
Neural likelihood estimation methods for simulation-based inference can suffer from performance degradation when the modeled data is very high-dimensional or lies along a lower-dim…