2 citations · 2 across the 6 of their papers we have counts for
7 papers
Diffusion-Augmented Markov Decision Processes for Maximum Entropy Reinforcement Learning
Sebastian Sanokowski, Kaustubh Patil, Majid Khadiv
Diffusion models provide an expressive framework for sampling from complex, unnormalized distributions. In this work, we extend Maximum Entropy Reinforcement Learning (ME-RL) to di…
Rethinking Losses for Diffusion Bridge Samplers
Sebastian Sanokowski, Lukas Gruber, Christoph Bartmann +2
Diffusion bridges are a promising class of deep-learning methods for sampling from unnormalized distributions. Recent works show that the Log Variance (LV) loss consistently outper…
Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics
Sebastian Sanokowski, Wilhelm Berghammer, Martin Ennemoser +3
Learning to sample from complex unnormalized distributions over discrete domains emerged as a promising research direction with applications in statistical physics, variational inf…
A Diffusion Model Framework for Unsupervised Neural Combinatorial Optimization
Sebastian Sanokowski, Sepp Hochreiter, Sebastian Lehner
Learning to sample from intractable distributions over discrete sets without relying on corresponding training data is a central problem in a wide range of fields, including Combin…
Geometry-Informed Neural Networks
Arturs Berzins, Andreas Radler, Eric Volkmann +3
Geometry is a ubiquitous tool in computer graphics, design, and engineering. However, the lack of large shape datasets limits the application of state-of-the-art supervised learnin…
Variational Annealing on Graphs for Combinatorial Optimization
Sebastian Sanokowski, Wilhelm Berghammer, Sepp Hochreiter +1
Several recent unsupervised learning methods use probabilistic approaches to solve combinatorial optimization (CO) problems based on the assumption of statistically independent sol…
Implicit recurrent networks: A novel approach to stationary input processing with recurrent neural networks in deep learning
Sebastian Sanokowski
The brain cortex, which processes visual, auditory and sensory data in the brain, is known to have many recurrent connections within its layers and from higher to lower layers. But…