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20212026
most citedConvolutional Neural Network Models and Interpretability for the Anisotropic Reynolds Stress Tensor in Turbulent One-dimensional Flows

3 citations · 7 across the 22 of their papers we have counts for

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Showing 2025 · cs.LGShow all

6 papers · 2 filters

cs.LG2025

Assessing the Geographic Generalization and Physical Consistency of Generative Models for Climate Downscaling

Carlo Saccardi, Maximilian Pierzyna, Haitz Sáez de Ocáriz Borde +6

Kilometer-scale weather data is crucial for real-world applications but remains computationally intensive to produce using traditional weather simulations. An emerging solution is…

cs.LG2025

Bridging Graph and State-Space Modeling for Intensive Care Unit Length of Stay Prediction

Shuqi Zi, Haitz Sáez de Ocáriz Borde, Emma Rocheteau +1

Predicting a patient's length of stay (LOS) in the intensive care unit (ICU) is a critical task for hospital resource management, yet remains challenging due to the heterogeneous a…

cs.LG2025

Mathematical Foundations of Geometric Deep Learning

Haitz Sáez de Ocáriz Borde, Michael Bronstein

We review the key mathematical concepts necessary for studying Geometric Deep Learning.

cs.LG2025

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs

Reza Arabpour, Haitz Sáez de Ocáriz Borde, Anastasis Kratsios

Low-Rank Adapters (LoRAs) have transformed the fine-tuning of Large Language Models (LLMs) by enabling parameter-efficient updates. However, their widespread adoption remains limit…

cs.LG2025

Beyond Parallelism: Synergistic Computational Graph Effects in Multi-Head Attention

Haitz Sáez de Ocáriz Borde

Multi-head attention powers Transformer networks, the primary deep learning architecture behind the success of large language models (LLMs). Yet, the theoretical advantages of mult…

cs.LG2025

Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement

Huidong Liang, Haitz Sáez de Ocáriz Borde, Baskaran Sripathmanathan +2

Long-range dependencies are critical for effective graph representation learning, yet most existing datasets focus on small graphs tailored to inductive tasks, offering limited ins…