{"id":"1906.00588","title":"Quantifying Point-Prediction Uncertainty in Neural Networks via Residual Estimation with an I/O Kernel","url":"https://codeofpaper.com/paper/1906.00588","arxiv_url":"https://arxiv.org/abs/1906.00588","published":"2019-06-03","code":"official","best":{"full_name":"cognizant-ai-labs/rio-paper","url":"https://github.com/cognizant-ai-labs/rio-paper","tier":"official","stars":9,"license":null,"framework":null,"language":"Python","last_commit":"2023-12-10T14:36:39"},"most_starred_community":null,"implementations":[{"full_name":"cognizant-ai-labs/rio-paper","url":"https://github.com/cognizant-ai-labs/rio-paper","tier":"official","stars":9,"license":null,"framework":null,"language":"Python","last_commit":"2023-12-10T14:36:39"}],"possible_matches":0}