295 citations · 657 across the 17 of their papers we have counts for
4 papers · 1 filter
On the Adversarial Robustness of Mixture of Experts
Joan Puigcerver, Rodolphe Jenatton, Carlos Riquelme +2
Adversarial robustness is a key desirable property of neural networks. It has been empirically shown to be affected by their sizes, with larger networks being typically more robust…
Plex: Towards Reliability using Pretrained Large Model Extensions
Dustin Tran, Jeremiah Liu, Michael W. Dusenberry +23
A recent trend in artificial intelligence is the use of pretrained models for language and vision tasks, which have achieved extraordinary performance but also puzzling failures. P…
Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts
Basil Mustafa, Carlos Riquelme, Joan Puigcerver +2
Large sparsely-activated models have obtained excellent performance in multiple domains. However, such models are typically trained on a single modality at a time. We present the L…
Transfer and Marginalize: Explaining Away Label Noise with Privileged Information
Mark Collier, Rodolphe Jenatton, Efi Kokiopoulou +1
Supervised learning datasets often have privileged information, in the form of features which are available at training time but are not available at test time e.g. the ID of the a…