3 citations · 5 across the 5 of their papers we have counts for
5 papers
Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters
Anastasis Kratsios, Tin Sum Cheng, Aurelien Lucchi +1
Low-Rank Adaptation (LoRA) has emerged as a widely adopted parameter-efficient fine-tuning (PEFT) technique for foundation models. Recent work has highlighted an inherent asymmetry…
Projections of Model Spaces for Latent Graph Inference
Haitz Sáez de Ocáriz Borde, Álvaro Arroyo, Ingmar Posner
Graph Neural Networks leverage the connectivity structure of graphs as an inductive bias. Latent graph inference focuses on learning an adequate graph structure to diffuse informat…
Aerothermodynamic Simulators for Rocket Design using Neural Fields
Haitz Sáez de Ocáriz Borde, Pietro Innocenzi, Flavio Savarino
The typical size of computational meshes needed for realistic geometries and high-speed flow conditions makes Computational Fluid Dynamics (CFD) impractical for full-mission perfor…
Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression
Haitz Sáez de Ocáriz Borde, Federico Barbero
We demonstrate the applicability of model-agnostic algorithms for meta-learning, specifically Reptile, to GNN models in molecular regression tasks. Using meta-learning we are able…
Interpretability in deep learning for finance: a case study for the Heston model
Damiano Brigo, Xiaoshan Huang, Andrea Pallavicini +1
Deep learning is a powerful tool whose applications in quantitative finance are growing every day. Yet, artificial neural networks behave as black boxes and this hinders validation…