Uncertainty Modeling
Yoshua explains the importance of capturing uncertainty in reward functions using Bayesian methods. Gflownets aim to minimize uncertainty and discover various modes efficiently. The discussion delves into the challenges and potential of Gflownets in exploring unknown areas effectively.In this clip
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Machine Learning Street Talk (MLST)
#063 - Prof. YOSHUA BENGIO - GFlowNets, Consciousness & Causality
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