Haitz Sáez de Ocáriz Borde

According to our database, Haitz Sáez de Ocáriz Borde authored at least 19 papers between 2021 and 2024.

Collaborative distances:

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

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

Neural Spacetimes for DAG Representation Learning.

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

CoRR, 2024

Metric Learning for Clifford Group Equivariant Neural Networks.

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

CoRR, 2024

Score Distillation via Reparametrized DDIM.

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

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

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

AMES: A Differentiable Embedding Space Selection Framework for Latent Graph Inference.

Yuan Lu, Haitz Sáez de Ocáriz Borde, Pietro Liò

CoRR, 2023

Closed-Form Diffusion Models.

Christopher Scarvelis, Haitz Sáez de Ocáriz Borde, Justin Solomon

CoRR, 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

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