3 accepted papers
Score-based generative modeling through differential equations has driven breakthroughs in high-fidelity image synthesis, offering modular model design and efficient sampling. However, this success has not been widely translated to timeseries forecasting yet. This gap stems from the sequential natur…
Concepts such as objects, patterns, and shapes are how humans understand the world. Building on this intuition, concept-based explainability methods aim to study representations learned by deep neural networks in relation to human-understandable concepts. Here, Concept Activation Vectors (CAVs) are…
In image segmentation, the classic approach of learning a deterministic segmentation neither accounts for noise and ambiguity in the data nor for expert disagreements about the correct segmentation. This has been addressed by architectures that predict heteroscedastic (input-dependent) segmentation…