2 citations · 4 across the 8 of their papers we have counts for
9 papers
Broadband Ground-Motion Synthesis by Conditioning Denoising Diffusion Models on Low-Frequency Waveforms
Kadek Hendrawan Palgunadi, Simon Dirmeier, Maria Koroni +2
Seismic hazard and structural analyses require broadband ground-motion time histories, yet the high-frequency band (above 1~Hz) is the hardest to obtain: physics-based simulations…
Causal Posterior Estimation
Simon Dirmeier, Antonietta Mira
We present Causal Posterior Estimation (CPE), a novel method for Bayesian inference in simulator models, where evaluating the likelihood function is intractable or computationally…
A thermodynamic approach to Approximate Bayesian Computation with multiple summary statistics
Carlo Albert, Simone Ulzega, Simon Dirmeier +3
Bayesian inference with stochastic models is often difficult because their likelihood functions involve high-dimensional integrals. Approximate Bayesian Computation (ABC) avoids ev…
Simulation-based Inference with the Python Package sbijax
Simon Dirmeier, Antonietta Mira, Carlo Albert
Neural simulation-based inference (SBI) describes an emerging family of methods for Bayesian inference with intractable likelihood functions that use neural networks as surrogate m…
Synthetic location trajectory generation using categorical diffusion models
Simon Dirmeier, Ye Hong, Fernando Perez-Cruz
Diffusion probabilistic models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data, for instance, for computer vision, a…
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…