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Self-supervised learning models extract general-purpose representations from data.
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What uncertainties do we need in bayesian deep learning for computer vision?
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Averaging weights leads to wider optima and better generalization
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Clar: Contrastive learning of auditory representations
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On the opportunities and risks of foundation models
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Randomized prior functions for deep reinforcement learning
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Laplace redux-effortless Bayesian deep learning
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Audio-visual instance discrimination with cross-modal agreement
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Learning transferable visual models from natural language supervision
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Temperature as uncertainty in contrastive learning
Oliver Zhang, Mike Wu, Jasmine Bayrooti, and Noah Goodman · 2021
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Uncertainty-aware meta-learning for multimodal task distributions
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Hossein Mirzaei, Mohammadreza Salehi, Sajjad Shahabi, Efstratios Gavves, Cees GM Snoek, Mohammad Sabokrou, and Mohammad Hossein Rohban · 2022
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Chatgpt: Optimizing language models for dialogue
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Plex: Towards reliability using pretrained large model extensions
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