no code implementations • 22 Jan 2024 • Tatiana Lopez-Guevara, Yulia Rubanova, William F. Whitney, Tobias Pfaff, Kimberly Stachenfeld, Kelsey R. Allen
Accurately simulating real world object dynamics is essential for various applications such as robotics, engineering, graphics, and design.
no code implementations • 8 Dec 2023 • William F. Whitney, Tatiana Lopez-Guevara, Tobias Pfaff, Yulia Rubanova, Thomas Kipf, Kimberly Stachenfeld, Kelsey R. Allen
Realistic simulation is critical for applications ranging from robotics to animation.
no code implementations • 7 Dec 2022 • Kelsey R. Allen, Yulia Rubanova, Tatiana Lopez-Guevara, William Whitney, Alvaro Sanchez-Gonzalez, Peter Battaglia, Tobias Pfaff
Simulating rigid collisions among arbitrary shapes is notoriously difficult due to complex geometry and the strong non-linearity of the interactions.
2 code implementations • 22 Sep 2022 • Borja Ibarz, Vitaly Kurin, George Papamakarios, Kyriacos Nikiforou, Mehdi Bennani, Róbert Csordás, Andrew Dudzik, Matko Bošnjak, Alex Vitvitskyi, Yulia Rubanova, Andreea Deac, Beatrice Bevilacqua, Yaroslav Ganin, Charles Blundell, Petar Veličković
The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks, especially in a way that generalises out of distribution.
no code implementations • 16 Dec 2021 • Yulia Rubanova, Alvaro Sanchez-Gonzalez, Tobias Pfaff, Peter Battaglia
We can improve the simulation accuracy on a larger system by applying more solver iterations at test time.
no code implementations • ICLR 2022 • Jonathan Godwin, Michael Schaarschmidt, Alexander L Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Veličković, James Kirkpatrick, Peter Battaglia
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
1 code implementation • 15 Jun 2021 • Jonathan Godwin, Michael Schaarschmidt, Alexander Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Veličković, James Kirkpatrick, Peter Battaglia
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
1 code implementation • NeurIPS 2019 • Yulia Rubanova, Tian Qi Chen, David K. Duvenaud
Time series with non-uniform intervals occur in many applications, and are difficult to model using standard recurrent neural networks (RNNs).
12 code implementations • 8 Jul 2019 • Yulia Rubanova, Ricky T. Q. Chen, David Duvenaud
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
55 code implementations • NeurIPS 2018 • Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud
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