Cikkek nyilvánosan hozzáférhető megbízással - Javier AntoranTovábbi információ
Valahol hozzáférhető: 14
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
U Bhatt, J Antorán, Y Zhang, QV Liao, P Sattigeri, R Fogliato, ...
2021 AAAI/ACM Conference on AI, Ethics, and Society, 2020
Megbízások: UK Engineering and Physical Sciences Research Council
Depth uncertainty in neural networks
J Antorán, JU Allingham, JM Hernández-Lobato
Advances in Neural Information Processing Systems 33 (NeurIPS 2020), 2020
Megbízások: UK Engineering and Physical Sciences Research Council
Bayesian Deep Learning via Subnetwork Inference
E Daxberger, E Nalisnick, JU Allingham, J Antorán, ...
International Conference on Machine Learning, 2021, 2020
Megbízások: UK Engineering and Physical Sciences Research Council
Adapting the linearised laplace model evidence for modern deep learning
J Antorán, D Janz, JU Allingham, E Daxberger, RR Barbano, E Nalisnick, ...
International Conference on Machine Learning, 796-821, 2022
Megbízások: UK Engineering and Physical Sciences Research Council
Sampling from Gaussian process posteriors using stochastic gradient descent
JA Lin, J Antorán, S Padhy, D Janz, JM Hernández-Lobato, A Terenin
Advances in Neural Information Processing Systems 36, 36886-36912, 2023
Megbízások: UK Engineering and Physical Sciences Research Council
SE (3) equivariant augmented coupling flows
L Midgley, V Stimper, J Antorán, E Mathieu, B Schölkopf, ...
Advances in Neural Information Processing Systems 36, 2024
Megbízások: UK Engineering and Physical Sciences Research Council
Disentangling and learning robust representations with natural clustering
J Antoran, A Miguel
2019 18th IEEE International Conference On Machine Learning And Applications …, 2019
Megbízások: Government of Spain
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior
J Antorán, R Barbano, J Leuschner, JM Hernández-Lobato, B Jin
arXiv preprint arXiv:2203.00479, 2022
Megbízások: German Research Foundation, UK Engineering and Physical Sciences Research …
Linearised laplace inference in networks with normalisation layers and the neural g-prior
J Antorán, JU Allingham, D Janz, E Daxberger, E Nalisnick, ...
Fourth Symposium on Advances in Approximate Bayesian Inference, 2022
Megbízások: UK Engineering and Physical Sciences Research Council
A probabilistic deep image prior over image space
R Barbano, J Antorán, JM Hernández-Lobato, B Jin
Fourth Symposium on Advances in Approximate Bayesian Inference, 2022
Megbízások: UK Engineering and Physical Sciences Research Council
Fast and Painless Image Reconstruction in Deep Image Prior Subspaces.
R Barbano, J Antorán, J Leuschner, JM Hernández-Lobato, Z Kereta, ...
arXiv preprint arXiv:2302.10279, 2023
Megbízások: German Research Foundation, UK Engineering and Physical Sciences Research …
Learning generative models with invariance to symmetries
JU Allingham, J Antoran, S Padhy, E Nalisnick, JM Hernández-Lobato
NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations, 2022
Megbízások: UK Engineering and Physical Sciences Research Council
Depth Uncertainty Networks for Active Learning
C Murray, JU Allingham, J Antorán, JM Hernández-Lobato
Megbízások: UK Engineering and Physical Sciences Research Council
Amortised Variational Inference for Hierarchical Mixture Models
J Antorán, J Yao, W Pan, JM Hernández-Lobato, F Doshi-Velez
Megbízások: US National Science Foundation, UK Engineering and Physical Sciences …
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