5 citations · 7 across the 5 of their papers we have counts for
5 papers · 1 filter
On the Necessity of Learnable Sheaf Laplacians
Ferran Hernandez Caralt, Mar Gonzàlez i Català, Adrián Bazaga +1
Sheaf Neural Networks (SNNs) were introduced as an extension of Graph Convolutional Networks to address oversmoothing on heterophilous graphs by attaching a sheaf to the input grap…
Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models
Adrián Bazaga, Rexhina Blloshmi, Bill Byrne +1
Large Language Models (LLMs) have emerged as powerful tools for generating coherent text, understanding context, and performing reasoning tasks. However, they struggle with tempora…
TabMDA: Tabular Manifold Data Augmentation for Any Classifier using Transformers with In-context Subsetting
Andrei Margeloiu, Adrián Bazaga, Nikola Simidjievski +2
Tabular data is prevalent in many critical domains, yet it is often challenging to acquire in large quantities. This scarcity usually results in poor performance of machine learnin…
FLUID-LLM: Learning Computational Fluid Dynamics with Spatiotemporal-aware Large Language Models
Max Zhu, Adrián Bazaga, Pietro Liò
Learning computational fluid dynamics (CFD) traditionally relies on computationally intensive simulations of the Navier-Stokes equations. Recently, large language models (LLMs) hav…
HyperBERT: Mixing Hypergraph-Aware Layers with Language Models for Node Classification on Text-Attributed Hypergraphs
Adrián Bazaga, Pietro Liò, Gos Micklem
Hypergraphs are characterized by complex topological structure, representing higher-order interactions among multiple entities through hyperedges. Lately, hypergraph-based deep lea…