HYPERPARAMETER ENSEMBLES FOR ROBUSTNESS AND UNCERTAINTY QUANTIFICATION

von Wenzel, Florian
Veröffentlicht in NeurIPS (34. : 2020 : Online) 34th Conference on Neural Information Processing Systems (NeurIPS 2020) ; Volume 8 of 27 (2021)

Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts

von Mustafa, Basil
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 13 of 50 (2023)

On the Adversarial Robustness of Mixture of Experts

von Puigcerver, Joan
Veröffentlicht in NeurIPS (36. : 2022 : New Orleans, La.; Online) 36th Conference on Neural Information Processing Systems (NeurIPS 2022 ; Volume 13 of 50 (2023)

LEARNING SEARCH SPACES FOR BAYESIAN OPTIMIZATION: ANOTHER VIEW OF HYPERPARAMETER TRANSFER LEARNING

von Perrone, Valerio
Veröffentlicht in NeurIPS (33. : 2019 : Vancouver, British Columbia) 32nd Conference on Neural Information Processing Systems (NeurIPS 2019) ; Volume 17 of 20 (2020)

Scaling Vision with Sparse Mixture of Experts

von Riquelme, Carlos
Veröffentlicht in NeurIPS (35. : 2021 : Online) 35th Conference on Neural Information Processing Systems (NeurIPS 2021) ; Volume 11 of 36 (2022)

THE K-TIED NORMAL DISTRIBUTION: A COMPACT PARAMETERIZATION OF GAUSSIAN MEAN FIELD POSTERIORS IN BAYESIAN NEURAL NETWORKS

von Swiatkowski, Jakub
Veröffentlicht in International Conference on Machine Learning (37. : 2020 : Online) 37th International Conference on Machine Learning (ICML 2020) ; Part 12 of 15 (2021)

HOW GOOD IS THE BAYES POSTERIOR IN DEEP NEURAL NETWORKS REALLY?

von Wenzel, Florian
Veröffentlicht in International Conference on Machine Learning (37. : 2020 : Online) 37th International Conference on Machine Learning (ICML 2020) ; Part 14 of 15 (2021)