| Sheaf neural networks with connection laplacians F Barbero, C Bodnar, HS de Ocáriz Borde, M Bronstein, P Veličković, ... Proceedings of Topological, Algebraic, and Geometric Learning Workshops 2022 …, 2022 | 33 | 2022 |
| Sheaf attention networks F Barbero, C Bodnar, HS de Ocáriz Borde, P Lio NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations, 2022 | 18 | 2022 |
| Latent Graph Inference using Product Manifolds HS de Ocáriz Borde, A Kazi, F Barbero, P Lio International Conference on Learning Representations (ICLR 2023), 2023 | 16* | 2023 |
| Convolutional neural network models and interpretability for the anisotropic reynolds stress tensor in turbulent one-dimensional flows H Sáez de Ocáriz Borde, D Sondak, P Protopapas Journal of Turbulence 23 (1-2), 1-28, 2022 | 15 | 2022 |
| An Overview of Trees in Blockchain Technology: Merkle Trees and Merkle Patricia Tries H Sáez de Ocáriz Borde ResearchGate, 2022 | 13* | 2022 |
| Projections of Model Spaces for Latent Graph Inference H Sáez de Ocáriz Borde, A Arroyo, I Posner ICLR 2023 Workshop on Physics for Machine Learning, 2023 | 8* | 2023 |
| Interpretability in deep learning for finance: a case study for the Heston model D Brigo, X Huang, A Pallavicini, H Sáez de Ocáriz Borde arXiv preprint arXiv:2104.09476, 2021 | 6 | 2021 |
| Closed-form diffusion models C Scarvelis, HSO Borde, J Solomon arXiv preprint arXiv:2310.12395, 2023 | 5 | 2023 |
| Multi-task learning based convolutional models with curriculum learning for the anisotropic reynolds stress tensor in turbulent duct flow HS de Ocáriz Borde, D Sondak, P Protopapas ArXiv, abs/2111.00328, 2021 | 5 | 2021 |
| Asymmetry in Low-Rank Adapters of Foundation Models J Zhu, K Greenewald, K Nadjahi, HSO Borde, RB Gabrielsson, L Choshen, ... International Conference on Machine Learning (ICML), 2024, 2024 | 3 | 2024 |
| Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression H Sáez de Ocáriz Borde, F Barbero The First Learning on Graphs Conference, 2022 | 2 | 2022 |
| Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries HSO Borde, A Kratsios International Conference on Learning Representations (ICLR 2024), 2024 | 1 | 2024 |
| Neural Latent Geometry Search: Product Manifold Inference via Gromov-Hausdorff-Informed Bayesian Optimization HS de Ocáriz Borde, A Arroyo, IM López, I Posner, X Dong 37th Conference on Neural Information Processing Systems (NeurIPS 2023), 2023 | 1 | 2023 |
| Latent Space based Memory Replay for Continual Learning in Artificial Neural Networks H Sáez de Ocáriz Borde arXiv e-prints, arXiv: 2111.13297, 2021 | 1* | 2021 |
| Score Distillation via Reparametrized DDIM A Lukoianov, HSO Borde, K Greenewald, VC Guizilini, T Bagautdinov, ... arXiv preprint arXiv:2405.15891, 2024 | | 2024 |
| DreamUp3D: Object-Centric Generative Models for Single-View 3D Scene Understanding and Real-to-Sim Transfer Y Wu, HS de Ocáriz Borde, J Collins, OP Jones, I Posner IEEE Robotics and Automation Letters, 2024 | | 2024 |
| Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts A Kratsios, HSO Borde, T Furuya, MT Law arXiv preprint arXiv:2402.03460, 2024 | | 2024 |
| Elucidating Graph Neural Networks, Transformers, and Graph Transformers HS de Ocáriz Borde | | 2024 |
| Capacity Bounds for Hyperbolic Neural Network Representations of Latent Tree Structures A Kratsios, R Hong, HSO Borde Neural Networks, 2024 - Elsevier, 2024 | | 2024 |
| AMES: A Differentiable Embedding Space Selection Framework for Latent Graph Inference Y Lu, HSO Borde, P Liò NeurIPS 2023 Workshop on Symmetry and Geometry in Neural Representations, 2023 | | 2023 |
Federico BarberoUniversity of Oxford在 trinity.ox.ac.uk 的电子邮件经过验证
