most citedNeural Spacetimes for DAG Representation Learning

2 citations · 2 across the 3 of their papers we have counts for

collaborators
Showing cs.LGShow all

2 papers · 1 filter

cs.LG2026

Conf-Gen: Conformal Uncertainty Quantification for Generative Models

Gabriel Loaiza-Ganem, Kevin Zhang, Wei Cui +2

Conformal prediction (CP) and its extension, conformal risk control (CRC), are established frameworks for quantifying uncertainty in supervised machine learning through formal guar…

cs.LG2024★ 2 cited

Neural Spacetimes for DAG Representation Learning

Haitz Sáez de Ocáriz Borde, Anastasis Kratsios, Marc T. Law +2

We propose a class of trainable deep learning-based geometries called Neural Spacetimes (NSTs), which can universally represent nodes in weighted directed acyclic graphs (DAGs) as…