Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries

23 October 2023

Papers citing "Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries"

6 / 6 papers shown

Title
Small Transformers Compute Universal Metric Embeddings Anastasis Kratsios Valentin Debarnot Ivan Dokmanić 44 11 0 14 Sep 2022
Heterogeneous manifolds for curvature-aware graph embedding Francesco Di Giovanni Giulia Luise M. Bronstein 49 23 0 02 Feb 2022
Optimal Approximation Rate of ReLU Networks in terms of Width and Depth Zuowei Shen Haizhao Yang Shijun Zhang 85 114 0 28 Feb 2021
Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data Ben Adcock Simone Brugiapaglia N. Dexter S. Moraga 16 29 0 11 Dec 2020
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 231 3,202 0 24 Nov 2016
Input Convex Neural Networks Brandon Amos Lei Xu J. Zico Kolter 163 596 0 22 Sep 2016