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Found 7 papers, 5 papers with code
Propagating Model Uncertainty through Filtering-based Probabilistic Numerical ODE Solvers
Filtering-based probabilistic numerical solvers for ordinary differential equations (ODEs), also known as ODE filters, have been established as efficient methods for quantifying numerical uncertainty in the solution of ODEs.
Scalable Mechanistic Neural Networks for Differential Equations and Machine Learning
We propose Scalable Mechanistic Neural Network (S-MNN), an enhanced neural network framework designed for scientific machine learning applications involving long temporal sequences.
Unifying Causal Representation Learning with the Invariance Principle
Our main contribution is to show that many existing causal representation learning approaches methodologically align the representation to known data symmetries.
Marrying Causal Representation Learning with Dynamical Systems for Science
Causal representation learning promises to extend causal models to hidden causal variables from raw entangled measurements.
A Sparsity Principle for Partially Observable Causal Representation Learning
Causal representation learning aims at identifying high-level causal variables from perceptual data.
Multi-View Causal Representation Learning with Partial Observability
We present a unified framework for studying the identifiability of representations learned from simultaneously observed views, such as different data modalities.
Active Learning in Gaussian Process State Space Model
We investigate active learning in Gaussian Process state-space models (GPSSM).
