Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Sastry, Chandramouli Shama"'
Previous works on depression detection use datasets collected in similar environments to train and test the models. In practice, however, the train and test distributions cannot be guaranteed to be identical. Distribution shifts can be introduced due
Externí odkaz:
http://arxiv.org/abs/2404.05071
Autor:
Dahl, George E., Schneider, Frank, Nado, Zachary, Agarwal, Naman, Sastry, Chandramouli Shama, Hennig, Philipp, Medapati, Sourabh, Eschenhagen, Runa, Kasimbeg, Priya, Suo, Daniel, Bae, Juhan, Gilmer, Justin, Peirson, Abel L., Khan, Bilal, Anil, Rohan, Rabbat, Mike, Krishnan, Shankar, Snider, Daniel, Amid, Ehsan, Chen, Kongtao, Maddison, Chris J., Vasudev, Rakshith, Badura, Michal, Garg, Ankush, Mattson, Peter
Training algorithms, broadly construed, are an essential part of every deep learning pipeline. Training algorithm improvements that speed up training across a wide variety of workloads (e.g., better update rules, tuning protocols, learning rate sched
Externí odkaz:
http://arxiv.org/abs/2306.07179
Normalizing flows model a complex target distribution in terms of a bijective transform operating on a simple base distribution. As such, they enable tractable computation of a number of important statistical quantities, particularly likelihoods and
Externí odkaz:
http://arxiv.org/abs/2202.11322
In this paper, we propose a new compositional tool that will generate a musical outline of speech recorded/provided by the user for use as a musical building block in their compositions. The tool allows any user to use their own speech to generate mu
Externí odkaz:
http://arxiv.org/abs/2108.01043
When presented with Out-of-Distribution (OOD) examples, deep neural networks yield confident, incorrect predictions. Detecting OOD examples is challenging, and the potential risks are high. In this paper, we propose to detect OOD examples by identify
Externí odkaz:
http://arxiv.org/abs/1912.12510
Publikováno v:
Collnet Journal of Scientometrics and Information Management; January 2016, Vol. 10 Issue: 1 p21-40, 20p
