We show that a model owner can artificially introduce uncertainty into their model and provide a corresponding detection mechanism.
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Updated
Jun 2, 2025 - Jupyter Notebook
We show that a model owner can artificially introduce uncertainty into their model and provide a corresponding detection mechanism.
Code for our paper analyzing the looseness of the upper bound on selective classification performance.
I submitted this paper to Interspeech 2018. The paper was not accepted. The reviewer comments are included in the repo.
Bayesian inference and model selection, Kalman and particle filters, Gibbs sampling, rejection sampling, Metropolis-Hastings
Checks if a Promise is resolved or rejected asynchronously
A Guzzle middleware to log request and responses automatically
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