Quick Summary: From Flat Minima to Numerically Nonvacuous Generalization Bounds via PAC-Bayes (Talk) The goal of machine learning algorithms is to produce predictors having the smallest possible risk (expected loss).
Pac Bayes Control For Obstacle Avoidance -
From Flat Minima to Numerically Nonvacuous Generalization Bounds via PAC-Bayes (Talk) The goal of machine learning algorithms is to produce predictors having the smallest possible risk (expected loss). Talk by Pascal Germain at NIPS 2012 Workshop Multi-trade-off in Machine Learning.
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- From Flat Minima to Numerically Nonvacuous Generalization Bounds via PAC-Bayes (Talk)
- The goal of machine learning algorithms is to produce predictors having the smallest possible risk (expected loss).
- Talk by Pascal Germain at NIPS 2012 Workshop Multi-trade-off in Machine Learning.
- This short video details the methods and results from a model predictive
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