3 citations · 7 across the 22 of their papers we have counts for
9 papers · 1 filter
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…
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…
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.
Structured Captions Improve Prompt Adherence in Text-to-Image Models (Re-LAION-Caption 19M)
Nicholas Merchant, Haitz Sáez de Ocáriz Borde, Andrei Cristian Popescu +1
We argue that generative text-to-image models often struggle with prompt adherence due to the noisy and unstructured nature of large-scale datasets like LAION-5B. This forces users…
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…
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…
Fine-Tuning Next-Scale Visual Autoregressive Models with Group Relative Policy Optimization
Matteo Gallici, Haitz Sáez de Ocáriz Borde
Fine-tuning pre-trained generative models with Reinforcement Learning (RL) has emerged as an effective approach for aligning outputs more closely with nuanced human preferences. In…
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…
Keep It Light! Simplifying Image Clustering Via Text-Free Adapters
Yicen Li, Haitz Sáez de Ocáriz Borde, Anastasis Kratsios +1
In the era of pre-trained models, effective classification can often be achieved using simple linear probing or lightweight readout layers. In contrast, many competitive clustering…