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Content series
TransferLab content is aggregated in series belonging to one or more of our areas of interest.
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Simulation and AI
AI techniques are fundamentally transforming the field of simulation by combining physics-based modeling with data-driven machine learning.
Classifier calibration
For many applications of probabilistic classifiers it is important that the predicted confidence vectors reflect true probabilities (one …
Data valuation
Attributions of value to training samples can be used to examine data, improve data acquisition, debug and improve models or compensate data …
Explainable AI
Large opaque models like neural networks require dedicated methods to study and interpret their behavior. In this series we review recent …
Formal verification under uncertainty
Being able to ensure that AI systems behave as intended is essential for their adoption in many areas. This series covers formal methods for …
Simulation-based Inference
Simulation-based inference (SBI) offers a powerful framework for Bayesian parameter estimation in intricate scientific simulations where …
Uncertainty Quantification
Uncertainty quantification (UQ) in machine learning is the practice of measuring or estimating uncertainty in models. It is a set of tools …
Data efficiency
Numerous applications of Machine Learning in business intelligence, process optimization, product development or sales do not benefit from …
Reinforcement Learning
Recent and popular advances in Reinforcement Learning are known to be data-hungry. Attempts to handle this deficit include developing …
Probabilistic Models
Uncertainty permeates all aspects of real-world agency: Perception is subject to uncertainty owing to partial observability and unreliable …
Robustness in ML
The robustness of a model is a measure of its stability with respect to perturbations of the input. We investigate and review recent …
Anomaly detection
Many industrial applications of automated decision-making involve the detection of anomalous behaviour. Precisely defining what this means …
Diffusion Models
Diffusion models (DM) have become the state of the art for sample quality in generative modelling. They work by sequentially corrupting …
Geometric deep learning
Specialized deep learning architectures exploit the intrinsic regularities arising from the underlying structure of the physical world. …
