Search Results for author:
Found 7 papers, 1 papers with code
Sampling-Free Probabilistic Deep State-Space Models
Many real-world dynamical systems can be described as State-Space Models (SSMs).
Can you text what is happening? Integrating pre-trained language encoders into trajectory prediction models for autonomous driving
In autonomous driving tasks, scene understanding is the first step towards predicting the future behavior of the surrounding traffic participants.
Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems
Furthermore, we propose structured approximations to the covariance matrices of the Gaussian components in order to scale up to systems with many agents.
Differentiable Implicit Layers
In this paper, we introduce an efficient backpropagation scheme for non-constrained implicit functions.
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes
Neural Stochastic Differential Equations model a dynamical environment with neural nets assigned to their drift and diffusion terms.
A Deterministic Approximation to Neural SDEs
Our deterministic approximation of the transition kernel is applicable to both training and prediction.
Differential Bayesian Neural Nets
Neural Ordinary Differential Equations (N-ODEs) are a powerful building block for learning systems, which extend residual networks to a continuous-time dynamical system.
