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"What must we do to advance the organizing power of mankind alongside the technological advances of our extraordinary age?" @robreich quoting Einstein at the panel on Foundation Models at @StanfordHAI livestream: https://stanford.io/3v7V8gS
Pretraining is ≈SoTA for domain adaptation: just do contrastive learning on *all* unlabeled data + finetune on source labels. Features are NOT domain-invariant, but disentangle class & domain info to enable transfer. Theory & exps: http://arxiv.org/abs/2204.00570 http://arxiv.org/abs/2204.02683
Jürgen Schmidhuber does it again! Cool to see these models that learn to modify or output weights of other models are being explored more and more. https://twitter.com/hardmaru/status/1514133028943306754
Unhappy with existing tooling for evaluating ML models? Look no further than our latest blog post from @EyubogluSabri introducing Domino: an approach for automatically discovering systematic errors in ML models. https://ai.stanford.edu/blog/domino/
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We Are Pleased to Welcome New Members of Our Faculty
Sanmi Koyejo who focuses on Trustworthy Machine Learning for Healthcare and Neuroscience
Carlos Guestrin who focuses on Machine Learning, Explainability, Fairness and ML Systems.