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Found 14 papers, 6 papers with code
Global Safe Sequential Learning via Efficient Knowledge Transfer
As transferable source knowledge is often available in safety critical experiments, we propose to consider transfer safe sequential learning to accelerate the learning of safety.
Hybrid Modeling Design Patterns
In this paper, we provide four base patterns that can serve as blueprints for combining data-driven components with domain knowledge into a hybrid approach.
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.
Combining Slow and Fast: Complementary Filtering for Dynamics Learning
This filtering technique combines two signals by applying a high-pass filter to one signal, and low-pass filtering the other.
Learning Interacting Dynamical Systems with Latent Gaussian Process ODEs
We study time uncertainty-aware modeling of continuous-time dynamics of interacting objects.
Safe Active Learning for Multi-Output Gaussian Processes
Multi-output regression problems are commonly encountered in science and engineering.
Traversing Time with Multi-Resolution Gaussian Process State-Space Models
These models have a natural interpretation as discretized stochastic differential equations, but inference for long sequences with fast and slow transitions is difficult.
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.
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties
Deep Gaussian Processes learn probabilistic data representations for supervised learning by cascading multiple Gaussian Processes.
Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds
Gaussian Processes (GPs) are a generic modelling tool for supervised learning.
It is all in the noise: Efficient multi-task Gaussian process inference with structured residuals
Multi-task prediction models are widely being used to couple regressors or classification models by sharing information across related tasks.
