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A Simple Baseline for Bayesian Uncertainty in Deep Learning
Wesley J. Maddox, Pavel Izmailov, Timur Garipov, Dmitry P. Vetrov, Andrew Gordon Wilson
Neural Information Processing Systems http://nips.cc/
learning
Consideranews recommendation website that, when presented with a new user, sequentially offers a selection of currently trending articles. Such asystem may only haveafewopportunities tomakerecommendations before the user decides to navigate away, leaving little time to correct for misspecified or underspecified prior knowledge.
Vector Quantization Prompting for Continual Learning Li Jiao
Recent top-performing approaches are prompt-based methods that utilize a set of learnable parameters ( i .
TowardsaUnified Information-Theoretic FrameworkforGeneralization
Let D be an unknown distribution on a spaceZ, and let H be a set of classifiers. Consider a (randomized) learning algorithmA = (An)n 1 that selects an elementˆh in H, based onn i.i.d.