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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

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5 results for “GDE”

Generative Distribution Embeddings: Lifting autoencoders to the space of distributions for multiscale representation learningcs.LG

Generative Distribution Embeddings learn multiscale representations of distributions.

problem Learning representations of entire distributions for multiscale reasoning.

method Introducing GDE framework that lifts autoencoders to the space of distributions, using conditional generative models and distributional invariance.

result GDEs learn predictive sufficient statistics embedded in Wasserstein space, recovering distances and trajectories for Gaussian and Gaussian mixture distributions.

Distributional Embeddings Generative Models Multiscale Representation Wasserstein Space Conditional Generative Models

2025-05-23abs ↗pdf ↗

Continuous-Depth Neural Models for Dynamic Graph Predictioncs.LG

Neural GDEs improve graph prediction by blending discrete structures and differential equations.

problem Dynamic graph prediction challenges in irregularly sampled data.

method Continuous-depth graph neural networks (GNNs) with Neural GDEs.

result Neural GDEs enhance performance across various applications.

graph neural networks dynamic graphs neural differential equations traffic forecasting genetic regulatory networks

2021-06-22abs ↗pdf ↗

Graph Neural Ordinary Differential Equationscs.LG

We introduce the framework of continuous--depth graph neural networks (GNNs). Graph neural ordinary differential equations (GDEs) are formalized as the counterpart to GNNs where the input-output relationship is determined by a continuum of GNN layers, blending discrete topological structures and differential equations.…

2019-11-18abs ↗pdf ↗

On the Joint Interaction of Models, Data, and Featurescs.LG

New tool analyzes how features interact with data and models.

problem Lack of theoretical understanding of features in deep learning.

method Introduced interaction tensor to empirically analyze feature interactions.

result Proposed conceptual framework for feature learning with closed-form derivations.

feature learning interaction tensor deep learning generalization error GDE

2023-06-07abs ↗pdf ↗

Generalized Dirichlet Energy and Graph Laplacians for Clustering Directed and Undirected Graphsstat.ML

New method clusters directed and undirected graphs without losing directional information.

problem Clustering directed graphs due to asymmetry in edge connectivity.

method Generalized Dirichlet Energy (GDE) and generalized spectral clustering (GSC).

result GSC outperforms existing methods in clustering accuracy and robustness.

spectral clustering graph theory random walks directed graphs undirected graphs

2022-03-07abs ↗pdf ↗