12 citations · 20 across the 4 of their papers we have counts for
5 papers · 1 filter
Sampling-Free Probabilistic Deep State-Space Models
Andreas Look, Melih Kandemir, Barbara Rakitsch +1
Many real-world dynamical systems can be described as State-Space Models (SSMs). In this formulation, each observation is emitted by a latent state, which follows first-order Marko…
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems
Andreas Look, Melih Kandemir, Barbara Rakitsch +1
Graph neural networks are often used to model interacting dynamical systems since they gracefully scale to systems with a varying and high number of agents. While there has been mu…
Differentiable Implicit Layers
Andreas Look, Simona Doneva, Melih Kandemir +2
In this paper, we introduce an efficient backpropagation scheme for non-constrained implicit functions. These functions are parametrized by a set of learnable weights and may optio…
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes
Manuel Haussmann, Sebastian Gerwinn, Andreas Look +2
Neural Stochastic Differential Equations model a dynamical environment with neural nets assigned to their drift and diffusion terms. The high expressive power of their nonlinearity…
Differential Bayesian Neural Nets
Andreas Look, Melih Kandemir
Neural Ordinary Differential Equations (N-ODEs) are a powerful building block for learning systems, which extend residual networks to a continuous-time dynamical system. We propose…