5 citations · 8 across the 10 of their papers we have counts for
3 papers · 1 filter
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…