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ORCIDHaitz Sáez de Ocáriz Borde
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2020 – today
- 2024
[j2]Anastasis Kratsios, Ruiyang Hong, Haitz Sáez de Ocáriz Borde:
Capacity bounds for hyperbolic neural network representations of latent tree structures. Neural Networks 178: 106420 (2024)- [j1]Yizhe Wu
, Haitz Sáez de Ocáriz Borde, Jack Collins, Oiwi Parker Jones, Ingmar Posner:
DreamUp3D: Object-Centric Generative Models for Single-View 3D Scene Understanding and Real-to-Sim Transfer. IEEE Robotics Autom. Lett. 9(4): 3291-3298 (2024) - [c5]Haitz Sáez de Ocáriz Borde, Anastasis Kratsios:
Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries. ICLR 2024 - [c4]Jiacheng Zhu, Kristjan H. Greenewald, Kimia Nadjahi, Haitz Sáez de Ocáriz Borde, Rickard Brüel Gabrielsson, Leshem Choshen, Marzyeh Ghassemi, Mikhail Yurochkin, Justin Solomon:
Asymmetry in Low-Rank Adapters of Foundation Models. ICML 2024 - [i19]Haitz Sáez de Ocáriz Borde, Takashi Furuya, Anastasis Kratsios, Marc T. Law:
Breaking the Curse of Dimensionality with Distributed Neural Computation. CoRR abs/2402.03460 (2024) - [i18]Yizhe Wu, Haitz Sáez de Ocáriz Borde, Jack Collins, Oiwi Parker Jones, Ingmar Posner:
DreamUp3D: Object-Centric Generative Models for Single-View 3D Scene Understanding and Real-to-Sim Transfer. CoRR abs/2402.16308 (2024) - [i17]Jiacheng Zhu, Kristjan H. Greenewald, Kimia Nadjahi, Haitz Sáez de Ocáriz Borde, Rickard Brüel Gabrielsson, Leshem Choshen, Marzyeh Ghassemi, Mikhail Yurochkin, Justin Solomon:
Asymmetry in Low-Rank Adapters of Foundation Models. CoRR abs/2402.16842 (2024) - [i16]Artem Lukoianov, Haitz Sáez de Ocáriz Borde, Kristjan H. Greenewald, Vitor Campagnolo Guizilini, Timur M. Bagautdinov, Vincent Sitzmann, Justin Solomon:
Score Distillation via Reparametrized DDIM. CoRR abs/2405.15891 (2024) - [i15]Riccardo Ali, Paulina Kulyte, Haitz Sáez de Ocáriz Borde, Pietro Liò:
Metric Learning for Clifford Group Equivariant Neural Networks. CoRR abs/2407.09926 (2024) - [i14]Haitz Sáez de Ocáriz Borde, Anastasis Kratsios, Marc T. Law, Xiaowen Dong, Michael M. Bronstein:
Neural Spacetimes for DAG Representation Learning. CoRR abs/2408.13885 (2024) - 2023
- [c3]Haitz Sáez de Ocáriz Borde, Anees Kazi, Federico Barbero, Pietro Liò:
Latent Graph Inference using Product Manifolds. ICLR 2023 - [c2]Haitz Sáez de Ocáriz Borde, Alvaro Arroyo, Ismael Morales, Ingmar Posner, Xiaowen Dong:
Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization. NeurIPS 2023 - [i13]Haitz Sáez de Ocáriz Borde, Alvaro Arroyo, Ingmar Posner:
Projections of Model Spaces for Latent Graph Inference. CoRR abs/2303.11754 (2023) - [i12]Anastasis Kratsios, Ruiyang Hong, Haitz Sáez de Ocáriz Borde:
Capacity Bounds for Hyperbolic Neural Network Representations of Latent Tree Structures. CoRR abs/2308.09250 (2023) - [i11]Haitz Sáez de Ocáriz Borde, Alvaro Arroyo, Ismael Morales, Ingmar Posner, Xiaowen Dong:
Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization. CoRR abs/2309.04810 (2023) - [i10]Haitz Sáez de Ocáriz Borde, Alvaro Arroyo, Ismael Morales, Ingmar Posner, Xiaowen Dong:
Gromov-Hausdorff Distances for Comparing Product Manifolds of Model Spaces. CoRR abs/2309.05678 (2023) - [i9]Christopher Scarvelis, Haitz Sáez de Ocáriz Borde, Justin Solomon:
Closed-Form Diffusion Models. CoRR abs/2310.12395 (2023) - [i8]Haitz Sáez de Ocáriz Borde, Anastasis Kratsios:
Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries. CoRR abs/2310.15003 (2023) - [i7]Yuan Lu, Haitz Sáez de Ocáriz Borde, Pietro Liò:
AMES: A Differentiable Embedding Space Selection Framework for Latent Graph Inference. CoRR abs/2311.11891 (2023) - 2022
- [c1]Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde, Michael M. Bronstein, Petar Velickovic, Pietro Liò:
Sheaf Neural Networks with Connection Laplacians. TAG-ML 2022: 28-36 - [i6]Federico Barbero, Cristian Bodnar, Haitz Sáez de Ocáriz Borde, Michael M. Bronstein, Petar Velickovic, Pietro Liò:
Sheaf Neural Networks with Connection Laplacians. CoRR abs/2206.08702 (2022) - [i5]Haitz Sáez de Ocáriz Borde, Federico Barbero:
Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression. CoRR abs/2209.13410 (2022) - [i4]Haitz Sáez de Ocáriz Borde, Anees Kazi, Federico Barbero, Pietro Liò:
Latent Graph Inference using Product Manifolds. CoRR abs/2211.16199 (2022) - 2021
- [i3]Damiano Brigo, Xiaoshan Huang, Andrea Pallavicini, Haitz Sáez de Ocáriz Borde:
Interpretability in deep learning for finance: a case study for the Heston model. CoRR abs/2104.09476 (2021) - [i2]Haitz Sáez de Ocáriz Borde, David Sondak, Pavlos Protopapas:
Multi-Task Learning based Convolutional Models with Curriculum Learning for the Anisotropic Reynolds Stress Tensor in Turbulent Duct Flow. CoRR abs/2111.00328 (2021) - [i1]Haitz Sáez de Ocáriz Borde:
Latent Space based Memory Replay for Continual Learning in Artificial Neural Networks. CoRR abs/2111.13297 (2021)
Coauthor Index
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