Probabilistic models and deep learning - bridging the gap
Forskningsprojekt, 2019
– 2024
In this project, we will develop theory and methods related to the interplay between probabilistic models and deep learning. More specifically, we intend to develop both new models and new inference and learning algorithms for applications where latent variables are naturally characterized using, for instance, probabilistic graphical models or stochastic processes, whereas data is from some domain where deep learning has been successful (e.g., images).
The family of problems that involve such interplay between deep learning and probabilistic models is general, and we expect to derive tools that are widely applicable. However, to make the research more concrete and to showcase the merits of the new methodology we will study three specific applications in more depth, each one of significant importance on its own: learning from weak annotations, dynamical systems with deep-learning-based likelihoods, and automated mitosis detection and counting in histopathology.
Deltagare
Lennart Svensson (kontakt)
Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik
Jakob Lindqvist
Chalmers, Elektroteknik, Signalbehandling och medicinsk teknik
Samarbetspartners
Linköpings universitet
Linköping, Sweden
Finansiering
Wallenberg AI, Autonomous Systems and Software Program
(Finansieringsperiod saknas)
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