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Found 6 papers, 0 papers with code
Resolving Memorization in Empirical Diffusion Model for Manifold Data in High-Dimensional Spaces
We show that choosing the inertia diffusion model sample distribution is an $O\left(n^{-\frac{2}{d+4}}\right)$ Wasserstein-1 approximation of a data distribution lying on a $C^2$ manifold of dimension $d$.
A Clifford Algebraic Approach to E(n)-Equivariant High-order Graph Neural Networks
In this paper, we introduce the Clifford Group Equivariant Graph Neural Networks (CG-EGNNs), a novel EGNN that enhances high-order message passing by integrating high-order local structures in the context of Clifford algebras.
Equivariant Polynomial Functional Networks
On the other hand, parameter-sharing-based NFNs built upon equivariant linear layers exhibit lower memory consumption and faster running time, yet their expressivity is limited due to the large size of the symmetric group of the input neural networks.
Equivariant Neural Functional Networks for Transformers
This paper systematically explores neural functional networks (NFN) for transformer architectures.
Demystifying the Token Dynamics of Deep Selective State Space Models
Based on these investigations, we propose two refinements for the model: excluding the convergent scenario and reordering tokens based on their importance scores, both aimed at improving practical performance.
GRAND++: Graph Neural Diffusion with A Source Term
We propose GRAph Neural Diffusion with a source term (GRAND++) for graph deep learning with a limited number of labeled nodes, i. e., low-labeling rate.
