MLGAN: a Meta-Learning based Generative Adversarial Network adapter for rare disease differentiation tasks

Author:

Li Rui1ORCID,Wen Andrew2ORCID,Gao Jing3ORCID,Liu Hongfang2ORCID

Affiliation:

1. State University of New York at Buffalo, Buffalo, NY, USA

2. The University of Texas Health Science Center at Houston, Houston, TX, USA

3. Purdue University, West Lafayette, IN, USA

Funder

National Center for Advancing Translational Science of the National Institutes of Health

Publisher

ACM

Reference36 articles.

1. Marcin Andrychowicz , Misha Denil , Sergio Gomez , Matthew W Hoffman , David Pfau , Tom Schaul , Brendan Shillingford , and Nando De Freitas . 2016. Learning to learn by gradient descent by gradient descent. Advances in neural information processing systems 29 ( 2016 ). Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas. 2016. Learning to learn by gradient descent by gradient descent. Advances in neural information processing systems 29 (2016).

2. Patient Subtyping via Time-Aware LSTM Networks

3. Learning from positive and unlabeled data: a survey

4. SMOTE: Synthetic Minority Over-sampling Technique

5. Edward Choi , Mohammad Taha Bahadori , Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart. 2016 . RETAIN : An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism. In Advances in Neural Information Processing Systems, D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett (Eds.), Vol. 29 . Curran Associates, Inc . Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart. 2016. RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism. In Advances in Neural Information Processing Systems, D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett (Eds.), Vol. 29. Curran Associates, Inc.

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