Resultado de búsqueda
Hace 3 días · Yarin Gal and Zoubin Ghahramani. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In ICML, pages 1050-1059. PMLR, 2016. Google Scholar Digital Library; Audun Jsang. Subjective Logic: A formalism for reasoning under uncertainty. Springer Publishing Company, Incorporated, 2018. Google Scholar
Hace 5 días · Gintare Karolina Dziugaite, Zoubin Ghahramani, and Daniel M Roy. 2016. A study of the effect of jpg compression on adversarial images. arXiv preprint arXiv:1608.00853 (2016). Google Scholar; Gamaleldin Elsayed, Shreya Shankar, Brian Cheung, Nicolas Papernot, Alexey Kurakin, Ian Goodfellow, and Jascha Sohl-Dickstein. 2018.
Hace 5 días · Gal and Ghahramani [2015] Yarin Gal and Zoubin Ghahramani. Bayesian convolutional neural networks with bernoulli approximate variational inference. arXiv preprint arXiv:1506.02158, 2015. Gelman and Rubin [1992] Andrew Gelman and Donald B Rubin. Inference from iterative simulation using multiple sequences. Statistical science, pages 457–472, 1992.
Zoubin Ghahramani, Max Welling, Corinna Cortes, Neil D. Lawrence, Kilian Q. Weinberger: Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada. 2014 [contents]
Hace 5 días · [28] Yarin Gal and Zoubin Ghahramani. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In international conference on machine learning, pages 1050–1059. PMLR, 2016. [29] Maryam Zokaeinikoo, Pooyan Kazemian, Prasenjit Mitra, and Soundar Kumara.
Hace 4 días · limited data and therefore the downstream extrapolation (Ghahramani,2015;Chen et al.,2022). Prior knowledge could be an important source of information to be employed to mitigate the impacts of limited data and efficiently guide the training and inference (Beer et al.,2013). Notably, rather than
Hace 5 días · omy, the lar gest labelled datasets are of the order. 10 3. [Porter. and Scaife, 2023]. In this work we present an evaluation of. Bayesian deep learning for radio astronomy, using the mor ...
