Search Results for author:
Found 10 papers, 5 papers with code
Scaling Face Interaction Graph Networks to Real World Scenes
Accurately simulating real world object dynamics is essential for various applications such as robotics, engineering, graphics, and design.
Learning 3D Particle-based Simulators from RGB-D Videos
Realistic simulation is critical for applications ranging from robotics to animation.
Learning rigid dynamics with face interaction graph networks
Simulating rigid collisions among arbitrary shapes is notoriously difficult due to complex geometry and the strong non-linearity of the interactions.
A Generalist Neural Algorithmic Learner
The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks, especially in a way that generalises out of distribution.
Constraint-based graph network simulator
We can improve the simulation accuracy on a larger system by applying more solver iterations at test time.
Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond
We introduce “Noisy Nodes”, a very simple technique for improved training of GNNs, in which we corrupt the input graph with noise, and add a noise correcting node-level loss.
Initial Structure to Relaxed Energy (IS2RE), Direct
Molecular Property Prediction
+1
Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond
From this observation we derive "Noisy Nodes", a simple technique in which we corrupt the input graph with noise, and add a noise correcting node-level loss.
Ranked #4 on
Initial Structure to Relaxed Energy (IS2RE)
on OC20
Latent Ordinary Differential Equations for Irregularly-Sampled Time Series
Time series with non-uniform intervals occur in many applications, and are difficult to model using standard recurrent neural networks (RNNs).
Latent ODEs for Irregularly-Sampled Time Series
Time series with non-uniform intervals occur in many applications, and are difficult to model using standard recurrent neural networks (RNNs).
Ranked #1 on
Multivariate Time Series Imputation
on MuJoCo
Multivariate Time Series Forecasting
Multivariate Time Series Imputation
+3
Neural Ordinary Differential Equations
Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network.
Ranked #2 on
Multivariate Time Series Imputation
on MuJoCo
Multivariate Time Series Forecasting
Multivariate Time Series Imputation
