Haitz Sáez de Ocáriz Borde

According to our database, Haitz Sáez de Ocáriz Borde authored at least 29 papers between 2021 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Mathematical Foundations of Geometric Deep Learning.

Haitz Sáez de Ocáriz Borde, Michael M. Bronstein

CoRR, August, 2025

Structured Captions Improve Prompt Adherence in Text-to-Image Models (Re-LAION-Caption 19M).

Nicholas Merchant, Haitz Sáez de Ocáriz Borde, Andrei Cristian Popescu, Carlos Garcia Jurado Suarez

CoRR, July, 2025

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

Haitz Sáez de Ocáriz Borde

CoRR, July, 2025

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

Reza Arabpour, Haitz Sáez de Ocáriz Borde, Anastasis Kratsios

CoRR, July, 2025

Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters.

Anastasis Kratsios, Tin Sum Cheng, Aurélien Lucchi, Haitz Sáez de Ocáriz Borde

CoRR, June, 2025

Fine-Tuning Next-Scale Visual Autoregressive Models with Group Relative Policy Optimization.

Matteo Gallici, Haitz Sáez de Ocáriz Borde

CoRR, May, 2025

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, Michael M. Bronstein, Xiaowen Dong

CoRR, March, 2025

Keep It Light! Simplifying Image Clustering Via Text-Free Adapters.

Yicen Li, Haitz Sáez de Ocáriz Borde, Anastasis Kratsios, Paul D. McNicholas

CoRR, February, 2025

Closed-Form Diffusion Models.

Christopher Scarvelis, Haitz Sáez de Ocáriz Borde, Justin Solomon

Trans. Mach. Learn. Res., 2025

Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts.

Anastasis Kratsios, Haitz Sáez de Ocáriz Borde, Takashi Furuya, Marc T. Law

Trans. Mach. Learn. Res., 2025

Neural Spacetimes for DAG Representation Learning.

Haitz Sáez de Ocáriz Borde, Anastasis Kratsios, Marc T. Law, Xiaowen Dong, Michael M. Bronstein

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

DreamUp3D: Object-Centric Generative Models for Single-View 3D Scene Understanding and Real-to-Sim Transfer.

Yizhe Wu, Haitz Sáez de Ocáriz Borde, Jack Collins, Oiwi Parker Jones, Ingmar Posner

IEEE Robotics Autom. Lett., 2024

Capacity bounds for hyperbolic neural network representations of latent tree structures.

Anastasis Kratsios, Ruiyang Hong, Haitz Sáez de Ocáriz Borde

Neural Networks, 2024

Scalable Message Passing Neural Networks: No Need for Attention in Large Graph Representation Learning.

Haitz Sáez de Ocáriz Borde, Artem Lukoianov, Anastasis Kratsios, Michael M. Bronstein, Xiaowen Dong

CoRR, 2024

Metric Learning for Clifford Group Equivariant Neural Networks.

Riccardo Ali, Paulina Kulyte, Haitz Sáez de Ocáriz Borde, Pietro Liò

CoRR, 2024

Breaking the Curse of Dimensionality with Distributed Neural Computation.

Haitz Sáez de Ocáriz Borde, Takashi Furuya, Anastasis Kratsios, Marc T. Law

CoRR, 2024

Score Distillation via Reparametrized DDIM.

Artem Lukoianov, Haitz Sáez de Ocáriz Borde, Kristjan H. Greenewald, Vitor Guizilini, Timur M. Bagautdinov, Vincent Sitzmann, Justin M. Solomon

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Asymmetry in Low-Rank Adapters of Foundation Models.

Jiacheng Zhu, Kristjan H. Greenewald, Kimia Nadjahi, Haitz Sáez de Ocáriz Borde, Rickard Brüel Gabrielsson, Leshem Choshen, Marzyeh Ghassemi, Mikhail Yurochkin, Justin Solomon

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries.

Haitz Sáez de Ocáriz Borde, Anastasis Kratsios

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Gromov-Hausdorff Distances for Comparing Product Manifolds of Model Spaces.

Haitz Sáez de Ocáriz Borde, Alvaro Arroyo, Ismael Morales, Ingmar Posner, Xiaowen Dong

CoRR, 2023

Projections of Model Spaces for Latent Graph Inference.

Haitz Sáez de Ocáriz Borde, Alvaro Arroyo, Ingmar Posner

CoRR, 2023

Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization.

Haitz Sáez de Ocáriz Borde, Alvaro Arroyo, Ismael Morales, Ingmar Posner, Xiaowen Dong

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

AMES: A differentiable embedding space selection framework for latent graph inference.

Yuan Lu, Haitz Sáez de Ocáriz Borde, Pietro Liò

Proceedings of the NeurIPS Workshop on Symmetry and Geometry in Neural Representations, 2023

Latent Graph Inference using Product Manifolds.

Haitz Sáez de Ocáriz Borde, Anees Kazi, Federico Barbero, Pietro Liò

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression.

Haitz Sáez de Ocáriz Borde, Federico Barbero

CoRR, 2022

Sheaf Neural Networks with Connection Laplacians.

Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde, Michael M. Bronstein, Petar Velickovic, Pietro Liò

Proceedings of the Topological, 2022

2021

Latent Space based Memory Replay for Continual Learning in Artificial Neural Networks.

Haitz Sáez de Ocáriz Borde

CoRR, 2021

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

CoRR, 2021

Interpretability in deep learning for finance: a case study for the Heston model.

Damiano Brigo, Xiaoshan Huang, Andrea Pallavicini, Haitz Sáez de Ocáriz Borde

CoRR, 2021