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Found 18 papers, 10 papers with code
FoMo Rewards: Can we cast foundation models as reward functions?
We explore the viability of casting foundation models as generic reward functions for reinforcement learning.
Euclidean, Projective, Conformal: Choosing a Geometric Algebra for Equivariant Transformers
The Geometric Algebra Transformer (GATr) is a versatile architecture for geometric deep learning based on projective geometric algebra.
Geometric Algebra Transformer
In this paper we introduce the Geometric Algebra Transformer (GATr), a general-purpose architecture for geometric data.
Rigid Body Flows for Sampling Molecular Crystal Structures
Normalizing flows (NF) are a class of powerful generative models that have gained popularity in recent years due to their ability to model complex distributions with high flexibility and expressiveness.
Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wall
Computational fluid dynamics (CFD) is a valuable asset for patient-specific cardiovascular-disease diagnosis and prognosis, but its high computational demands hamper its adoption in practice.
Deconfounded Imitation Learning
Standard imitation learning can fail when the expert demonstrators have different sensory inputs than the imitating agent.
Learning Lattice Quantum Field Theories with Equivariant Continuous Flows
We propose a novel machine learning method for sampling from the high-dimensional probability distributions of Lattice Field Theories, which is based on a single neural ODE layer and incorporates the full symmetries of the problem.
Weakly supervised causal representation learning
Learning high-level causal representations together with a causal model from unstructured low-level data such as pixels is impossible from observational data alone.
Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows
We propose a continuous normalizing flow for sampling from the high-dimensional probability distributions of Quantum Field Theories in Physics.
Mesh convolutional neural networks for wall shear stress estimation in 3D artery models
In this work, we propose to instead use mesh convolutional neural networks that directly operate on the same finite-element surface mesh as used in CFD.
Natural Graph Networks
A key requirement for graph neural networks is that they must process a graph in a way that does not depend on how the graph is described.
Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs
A common approach to define convolutions on meshes is to interpret them as a graph and apply graph convolutional networks (GCNs).
Gauge Equivariant Spherical CNNs
Spherical CNNs are convolutional neural networks that can process signals on the sphere, such as global climate and weather patterns or omnidirectional images.
Covariance in Physics and Convolutional Neural Networks
In this proceeding we give an overview of the idea of covariance (or equivariance) featured in the recent development of convolutional neural networks (CNNs).
Causal Confusion in Imitation Learning
Such discriminative models are non-causal: the training procedure is unaware of the causal structure of the interaction between the expert and the environment.
Reparameterizing Distributions on Lie Groups
Unfortunately, this research has primarily focused on distributions defined in Euclidean space, ruling out the usage of one of the most influential class of spaces with non-trivial topologies: Lie groups.
Topological Constraints on Homeomorphic Auto-Encoding
When doing representation learning on data that lives on a known non-trivial manifold embedded in high dimensional space, it is natural to desire the encoder to be homeomorphic when restricted to the manifold, so that it is bijective and continuous with a continuous inverse.
Explorations in Homeomorphic Variational Auto-Encoding
Our experiments show that choosing manifold-valued latent variables that match the topology of the latent data manifold, is crucial to preserve the topological structure and learn a well-behaved latent space.
