Maternus Herold | TransferLab — appliedAI Institute

Maternus Herold

Maternus has a background in Mathematics and Computer Science. After working as an AI Engineer for some time, he decided to dig deeper into the field of Uncertainty Quantification, and is now pursuing a PhD on Inverse UQ with a focus on applications in the fields of crashworthiness and passenger safety in cars.

Besides classical Bayesian methods to obtain a distribution on the parameters of interest, he currently explores the applicability of Simulation-Based Inference to his problem.

Maternus' work

Practical anomaly detection

An two-day introduction to unsupervised ML techniques for anomaly detection and their strengths and weaknesses in different application areas.

Amortized Bayesian Decision-Making for Simulation-Based Models

Bayesian inference is a popular tool for parameter estimation. However, the posterior distribution might not be sufficient for …

sbi: the simulation-based inference toolkit

sbi is a Python package for Bayesian parameter inference on simulators. It implements state-of-the-art algorithms and comes with …

Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation

Identifying the source distribution behind observed data is an ill-posed problem. Sourcerer [Vet24S] introduces a novel approach based on …

Flow Matching for Scalable Simulation-Based Inference

Via flow matching, continuous normalizing flows can be trained efficiently for the use in Simulation-based Inference. They yield …

Effects of XAI on perception, trust and acceptance

This talk delves into the influence of Explainable Artificial Intelligence (XAI) on human cognition, trust, and acceptance of AI-driven …

Automatic Posterior Transformation for Likelihood-free Inference

A sequential neural posterior estimation method that modifies the posterior approximation using arbitrary, dynamically updated proposals. …

Deep Learning with Functional Inputs

Functional data is common in many applications but has not been included into deep-learning methods naturally. This paper presents an input …

Density estimation using deep generative neural networks

Density estimation is among the fundamental problems in statistics. It is difficult to estimate the density of high-dimensional data due to …

Introduction to Simulation-based Inference

Embrace the challenges of intractable likelihoods with simulation-based inference. A half-day workshop introducing the concepts …

Spectral likelihood expansions for Bayesian inference

Spectral Likelihood expansion represents the Likelihood using Polynomial Chaos Expansion to speed up access to the posterior. It thus …

A Trust Crisis In Simulation-Based Inference? Your Posterior Approximations Can Be Unfaithful

In posterior estimation, it is generally worse to exclude plausible parameters than wrongly including implausible ones. However, extensive …

Benchmarking Simulation-Based Inference

A much-needed benchmark for methods of simulation-based inference.

The Frontier of Simulation-based Inference

An overview and schematic comparison of recent developments in simulation-based inference and their enabling factors. Advancements in ML, …

Monte Carlo-Dropout for Uncertainty Quantification in Deep Learning

Deep learning models are commonly used for regression tasks, producing a point estimate for a given set of inputs. However, a single point …

Sparse Random Feature Expansions for Surrogate Modelling

Surrogate models are indispensable tools for conducting uncertainty quantification and robustness analyses, thereby driving pivotal design …

Hamiltonian Monte Carlo Sampling

Metropolis, Slice or Gibbs sampling tend to be inefficient when applied in high dimensions due to their local random walk behaviour. …