Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Rubanova, Yulia"'
Autor:
Wu, Ziyi, Rubanova, Yulia, Kabra, Rishabh, Hudson, Drew A., Gilitschenski, Igor, Aytar, Yusuf, van Steenkiste, Sjoerd, Allen, Kelsey R., Kipf, Thomas
We address the problem of multi-object 3D pose control in image diffusion models. Instead of conditioning on a sequence of text tokens, we propose to use a set of per-object representations, Neural Assets, to control the 3D pose of individual objects
Externí odkaz:
http://arxiv.org/abs/2406.09292
Autor:
Rubanova, Yulia, Lopez-Guevara, Tatiana, Allen, Kelsey R., Whitney, William F., Stachenfeld, Kimberly, Pfaff, Tobias
Simulating large scenes with many rigid objects is crucial for a variety of applications, such as robotics, engineering, film and video games. Rigid interactions are notoriously hard to model: small changes to the initial state or the simulation para
Externí odkaz:
http://arxiv.org/abs/2405.14045
Autor:
Lopez-Guevara, Tatiana, Rubanova, Yulia, Whitney, William F., Pfaff, Tobias, Stachenfeld, Kimberly, Allen, Kelsey R.
Accurately simulating real world object dynamics is essential for various applications such as robotics, engineering, graphics, and design. To better capture complex real dynamics such as contact and friction, learned simulators based on graph networ
Externí odkaz:
http://arxiv.org/abs/2401.11985
Autor:
Whitney, William F., Lopez-Guevara, Tatiana, Pfaff, Tobias, Rubanova, Yulia, Kipf, Thomas, Stachenfeld, Kimberly, Allen, Kelsey R.
Realistic simulation is critical for applications ranging from robotics to animation. Traditional analytic simulators sometimes struggle to capture sufficiently realistic simulation which can lead to problems including the well known "sim-to-real" ga
Externí odkaz:
http://arxiv.org/abs/2312.05359
Autor:
Allen, Kelsey R., Rubanova, Yulia, Lopez-Guevara, Tatiana, Whitney, William, Sanchez-Gonzalez, Alvaro, Battaglia, Peter, Pfaff, Tobias
Simulating rigid collisions among arbitrary shapes is notoriously difficult due to complex geometry and the strong non-linearity of the interactions. While graph neural network (GNN)-based models are effective at learning to simulate complex physical
Externí odkaz:
http://arxiv.org/abs/2212.03574
Autor:
Ibarz, Borja, Kurin, Vitaly, Papamakarios, George, Nikiforou, Kyriacos, Bennani, Mehdi, Csordás, Róbert, Dudzik, Andrew, Bošnjak, Matko, Vitvitskyi, Alex, Rubanova, Yulia, Deac, Andreea, Bevilacqua, Beatrice, Ganin, Yaroslav, Blundell, Charles, Veličković, Petar
The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks, especially in a way that generalises out of distribution. While recent years have seen a surge in methodological improvements in this area, they mostly focused
Externí odkaz:
http://arxiv.org/abs/2209.11142
In the area of physical simulations, nearly all neural-network-based methods directly predict future states from the input states. However, many traditional simulation engines instead model the constraints of the system and select the state which sat
Externí odkaz:
http://arxiv.org/abs/2112.09161
Autor:
Godwin, Jonathan, Schaarschmidt, Michael, Gaunt, Alexander, Sanchez-Gonzalez, Alvaro, Rubanova, Yulia, Veličković, Petar, Kirkpatrick, James, Battaglia, Peter
In this paper we show that simple noise regularisation can be an effective way to address GNN oversmoothing. First we argue that regularisers addressing oversmoothing should both penalise node latent similarity and encourage meaningful node represent
Externí odkaz:
http://arxiv.org/abs/2106.07971
Time series with non-uniform intervals occur in many applications, and are difficult to model using standard recurrent neural networks (RNNs). We generalize RNNs to have continuous-time hidden dynamics defined by ordinary differential equations (ODEs
Externí odkaz:
http://arxiv.org/abs/1907.03907
We introduce a new family of deep neural network models. Instead of specifying a discrete sequence of hidden layers, we parameterize the derivative of the hidden state using a neural network. The output of the network is computed using a black-box di
Externí odkaz:
http://arxiv.org/abs/1806.07366