Google researchers introduce ‘Internal RL,’ a technique that steers an models' hidden activations to solve long-horizon tasks ...
Abstract: When prior sample data are limited and the distribution parameters (DP) of random inputs exhibit uncertainty, Bayesian theory can be utilized to update the distribution of DPs and the ...
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When reduced-sugar gummy startup Häppy Candy debuted last fall it launched a free sampling campaign online supported by nano-influencers to help drive foot traffic to local retailers, gather consumer ...
ABSTRACT: In real-world applications, datasets frequently contain outliers, which can hinder the generalization ability of machine learning models. Bayesian classifiers, a popular supervised learning ...
Abstract: In practical applications, sampled-data systems are often affected by unforeseen physical constraints that may cause deviations in the sampling interval from the expected value and result in ...
My first job in media was as an assistant at The American Prospect, a small political magazine in Washington, D.C., that offered a promising foothold in journalism. I helped with the print order, ...
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