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Found 8 papers, 5 papers with code
LiDAR View Synthesis for Robust Vehicle Navigation Without Expert Labels
We train a deep learning model, which takes a LiDAR scan as input and predicts the future trajectory as output.
The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions
In this paper, we propose a novel approximate Gaussian filtering and smoothing method which propagates low-rank approximations of the covariance matrices.
Large-scale machine-learning-assisted exploration of the whole materials space
Crystal-graph attention networks have emerged recently as remarkable tools for the prediction of thermodynamic stability and materials properties from unrelaxed crystal structures.
Machine Learning guided high-throughput search of non-oxide garnets
Garnets, known since the early stages of human civilization, have found important applications in modern technologies including magnetorestriction, spintronics, lithium batteries, etc.
ProbNum: Probabilistic Numerics in Python
Probabilistic numerical methods (PNMs) solve numerical problems via probabilistic inference.
Probabilistic ODE Solutions in Millions of Dimensions
Probabilistic solvers for ordinary differential equations (ODEs) have emerged as an efficient framework for uncertainty quantification and inference on dynamical systems.
Probabilistic Numerical Method of Lines for Time-Dependent Partial Differential Equations
Thereby, we extend the toolbox of probabilistic programs for differential equation simulation to PDEs.
A Probabilistic State Space Model for Joint Inference from Differential Equations and Data
Mechanistic models with differential equations are a key component of scientific applications of machine learning.
