SCAT: Second Chance Autoencoder for Textual Data

Autor: Goudarzvand, Somaieh, Gharibi, Gharib, Lee, Yugyung

We present a k-competitive learning approach for textual autoencoders named Second Chance Autoencoder (SCAT). SCAT selects the $k$ largest and smallest positive activations as the winner neurons, which gain the activation values of the loser neurons

Externí odkaz: http://arxiv.org/abs/2005.06632

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Mining news media for understanding public health concerns.

Autor: Zolnoori M; Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA., Huang M; Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA., Patten CA; Center for Clinical and Translational Science, Community Engagement Program, Mayo Clinic, Rochester, MN, USA.; Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA., Balls-Berry JE; Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.; Mayo Clinic College of Medicine and Science, Rochester, MN, USA., Goudarzvand S; School of Computing and Engineering, University of Missouri-Kansas, Kansas City, MO, USA., Brockman TA; Center for Clinical and Translational Science, Community Engagement Program, Mayo Clinic, Rochester, MN, USA.; Department of Psychiatry and Psychology, Mayo Clinic, Rochester, MN, USA., Sagheb E; Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA., Yao L; Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.

Publikováno v: Journal of clinical and translational science [J Clin Transl Sci] 2019 Oct 23; Vol. 5 (1), pp. e1. Date of Electronic Publication: 2019 Oct 23.