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Found 13 papers, 10 papers with code
Improved motif-scaffolding with SE(3) flow matching
The first is motif amortization, in which FrameFlow is trained with the motif as input using a data augmentation strategy.
Fast protein backbone generation with SE(3) flow matching
We present FrameFlow, a method for fast protein backbone generation using SE(3) flow matching.
Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics
Coarse-grained (CG) molecular dynamics enables the study of biological processes at temporal and spatial scales that would be intractable at an atomistic resolution.
Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design
Additionally, the model automatically determines the number of atoms in the linker and its attachment points to the input fragments.
Equivariant Diffusion for Molecule Generation in 3D
This work introduces a diffusion model for molecule generation in 3D that is equivariant to Euclidean transformations.
Multivariate Time Series Forecasting with Latent Graph Inference
This paper introduces a new approach for Multivariate Time Series forecasting that jointly infers and leverages relations among time series.
Computational Efficiency
Multivariate Time Series Forecasting
+1
A Study of Joint Graph Inference and Forecasting
We study a recent class of models which uses graph neural networks (GNNs) to improve forecasting in multivariate time series.
E(n) Equivariant Normalizing Flows
This paper introduces a generative model equivariant to Euclidean symmetries: E(n) Equivariant Normalizing Flows (E-NFs).
E(n) Equivariant Graph Neural Networks
This paper introduces a new model to learn graph neural networks equivariant to rotations, translations, reflections and permutations called E(n)-Equivariant Graph Neural Networks (EGNNs).
The Convolution Exponential and Generalized Sylvester Flows
Empirically, we show that the convolution exponential outperforms other linear transformations in generative flows on CIFAR10 and the graph convolution exponential improves the performance of graph normalizing flows.
Neural Enhanced Belief Propagation on Factor Graphs
In this work we first extend graph neural networks to factor graphs (FG-GNN).
Combining Generative and Discriminative Models for Hybrid Inference
A graphical model is a structured representation of the data generating process.
Few-Shot Learning with Graph Neural Networks
We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not.
