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Beyond Bayes-Optimality: DeepMind & Stanford’s Meta-Learning Approach Builds Risk & Ambiguity Sensitive Agents
Oct. 13, 2022, 11:02 p.m. | Synced
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In the new paper Beyond Bayes-Optimality: Meta-Learning What You Know You Don't Know, researchers from DeepMind and Stanford University use modified meta-training algorithms to build agents with risk- and ambiguity-sensitivity.
The post Beyond Bayes-Optimality: DeepMind & Stanford’s Meta-Learning Approach Builds Risk & Ambiguity Sensitive Agents first appeared on Synced.
agents ai artificial intelligence bayes deepmind deep-neural-networks machine learning machine learning & data science meta meta-learning ml research risk stanford technology
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