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Found 5 papers, 1 papers with code
D-Flow: Differentiating through Flows for Controlled Generation
Taming the generation outcome of state of the art Diffusion and Flow-Matching (FM) models without having to re-train a task-specific model unlocks a powerful tool for solving inverse problems, conditional generation, and controlled generation in general.
Mosaic-SDF for 3D Generative Models
Current diffusion or flow-based generative models for 3D shapes divide to two: distilling pre-trained 2D image diffusion models, and training directly on 3D shapes.
Equivariant Polynomials for Graph Neural Networks
This paper introduces an alternative expressive power hierarchy based on the ability of GNNs to calculate equivariant polynomials of a certain degree.
Frame Averaging for Invariant and Equivariant Network Design
For example, Euclidean motion invariant/equivariant graph or point cloud neural networks.
Global Attention Improves Graph Networks Generalization
This paper advocates incorporating a Low-Rank Global Attention (LRGA) module, a computation and memory efficient variant of the dot-product attention (Vaswani et al., 2017), to Graph Neural Networks (GNNs) for improving their generalization power.
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