3 citations · 7 across the 22 of their papers we have counts for
23 papers
Airfoil2Vec: Spectral Geometry-Conditioned Neural Surrogate Models for Airfoil Aerodynamics and a Downforce-Generating CFD Dataset
Haitz Sáez de Ocáriz Borde, Flavio Savarino, Andrei Cristian Popescu +3
We introduce a dataset of approximately 10,000 Reynolds-Averaged Navier-Stokes (RANS) simulations of steady, incompressible, two-dimensional subsonic flow around downforce-generati…
Algorithmic Foundations of Deep Learning: Complexity-Theoretic Rates and a Characterization of Universal Approximation
Anastasis Kratsios, Simone Brugiapaglia, Bum Jun Kim +2
Feedforward neural network (NN) expressivity is typically studied by emulating optimal basis-expansion schemes. While powerful, this perspective is incomplete: it primarily capture…
Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity
Anastasis Kratsios, Gregory Cousins, Haitz Sáez de Ocáriz Borde +2
We show that, in a precise sense, a broad class of feedforward neural networks learn (have finite sample complexity) in the PAC model: every fixed finite feedforward architecture w…
k-Maximum Inner Product Attention for Graph Transformers and the Expressive Power of GraphGPS
Jonas De Schouwer, Haitz Sáez de Ocáriz Borde, Xiaowen Dong
Graph transformers have shown promise in overcoming limitations of traditional graph neural networks, such as oversquashing and difficulties in modeling long-range dependencies. Ho…
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…
Scalable Message Passing Neural Networks: No Need for Attention in Large Graph Representation Learning
Haitz Sáez de Ocáriz Borde, Artem Lukoianov, Anastasis Kratsios +2
We propose Scalable Message Passing Neural Networks (SMPNNs) and demonstrate that, by integrating standard convolutional message passing into a Pre-Layer Normalization Transformer-…
Neural Spacetimes for DAG Representation Learning
Haitz Sáez de Ocáriz Borde, Anastasis Kratsios, Marc T. Law +2
We propose a class of trainable deep learning-based geometries called Neural Spacetimes (NSTs), which can universally represent nodes in weighted directed acyclic graphs (DAGs) as…
Score Distillation via Reparametrized DDIM
Artem Lukoianov, Haitz Sáez de Ocáriz Borde, Kristjan Greenewald +4
While 2D diffusion models generate realistic, high-detail images, 3D shape generation methods like Score Distillation Sampling (SDS) built on these 2D diffusion models produce cart…
Asymmetry in Low-Rank Adapters of Foundation Models
Jiacheng Zhu, Kristjan Greenewald, Kimia Nadjahi +6
Parameter-efficient fine-tuning optimizes large, pre-trained foundation models by updating a subset of parameters; in this class, Low-Rank Adaptation (LoRA) is particularly effecti…
Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts
Anastasis Kratsios, Haitz Sáez de Ocáriz Borde, Takashi Furuya +1
Mixture-of-Experts (MoEs) can scale up beyond traditional deep learning models by employing a routing strategy in which each input is processed by a single "expert" deep learning m…
AMES: A Differentiable Embedding Space Selection Framework for Latent Graph Inference
Yuan Lu, Haitz Sáez de Ocáriz Borde, Pietro Liò
In real-world scenarios, although data entities may possess inherent relationships, the specific graph illustrating their connections might not be directly accessible. Latent graph…
Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries
Haitz Sáez de Ocáriz Borde, Anastasis Kratsios
The inductive bias of a graph neural network (GNN) is largely encoded in its specified graph. Latent graph inference relies on latent geometric representations to dynamically rewir…
Capacity Bounds for Hyperbolic Neural Network Representations of Latent Tree Structures
Anastasis Kratsios, Ruiyang Hong, Haitz Sáez de Ocáriz Borde
We study the representation capacity of deep hyperbolic neural networks (HNNs) with a ReLU activation function. We establish the first proof that HNNs can -isometrical…
Multi-Task Learning based Convolutional Models with Curriculum Learning for the Anisotropic Reynolds Stress Tensor in Turbulent Duct Flow
Haitz Sáez de Ocáriz Borde, David Sondak, Pavlos Protopapas
The Reynolds-averaged Navier-Stokes (RANS) equations require accurate modeling of the anisotropic Reynolds stress tensor. Traditional closure models, while sophisticated, often onl…
Convolutional Neural Network Models and Interpretability for the Anisotropic Reynolds Stress Tensor in Turbulent One-dimensional Flows
Haitz Sáez de Ocáriz Borde, David Sondak, Pavlos Protopapas
The Reynolds-averaged Navier-Stokes (RANS) equations are widely used in turbulence applications. They require accurately modeling the anisotropic Reynolds stress tensor, for which…