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Found 7 papers, 1 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.
How does the primate brain combine generative and discriminative computations in vision?
The alternative conception is that of vision as an inference process in Helmholtz's sense, where the sensory evidence is evaluated in the context of a generative model of the causal processes giving rise to it.
Learning 3D Particle-based Simulators from RGB-D Videos
Realistic simulation is critical for applications ranging from robotics to animation.
Physical Design using Differentiable Learned Simulators
In our fluid manipulation tasks, the resulting designs outperformed those found by sampling-based optimization techniques.
Learned Coarse Models for Efficient Turbulence Simulation
We show that our proposed model can simulate turbulent dynamics more accurately than classical numerical solvers at the comparably low resolutions across various scientifically relevant metrics.
Graph Networks with Spectral Message Passing
Our model projects vertices of the spatial graph onto the Laplacian eigenvectors, which are each represented as vertices in a fully connected "spectral graph", and then applies learned message passing to them.
Object-oriented state editing for HRL
We introduce agents that use object-oriented reasoning to consider alternate states of the world in order to more quickly find solutions to problems.
