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Wonsung Lee

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3 papers

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AAAI Conference 2019 Conference Paper

Adversarial Dropout for Recurrent Neural Networks

Sungrae Park
Kyungwoo Song
Mingi Ji
Wonsung Lee
Il-Chul Moon

Successful application processing sequential data, such as text and speech, requires an improved generalization performance of recurrent neural networks (RNNs). Dropout techniques for RNNs were introduced to respond to these demands, but we conjecture that the dropout on RNNs could have been improved by adopting the adversarial concept. This paper investigates ways to improve the dropout for RNNs by utilizing intentionally generated dropout masks. Specifically, the guided dropout used in this research is called as adversarial dropout, which adversarially disconnects neurons that are dominantly used to predict correct targets over time. Our analysis showed that our regularizer, which consists of a gap between the original and the reconfigured RNNs, was the upper bound of the gap between the training and the inference phases of the random dropout. We demonstrated that minimizing our regularizer improved the effectiveness of the dropout for RNNs on sequential MNIST tasks, semi-supervised text classification tasks, and language modeling tasks.

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AAAI Conference 2018 Conference Paper

Neural Ideal Point Estimation Network

Kyungwoo Song
Wonsung Lee
Il-Chul Moon

Understanding politics is challenging because the politics take the inﬂuence from everything. Even we limit ourselves to the political context in the legislative processes; we need a better understanding of latent factors, such as legislators, bills, their ideal points, and their relations. From the modeling perspective, this is difﬁcult 1) because these observations lie in a high dimension that requires learning on low dimensional representations, and 2) because these observations require complex probabilistic modeling with latent variables to reﬂect the causalities. This paper presents a new model to reﬂect and understand this political setting, NIPEN, including factors mentioned above in the legislation. We propose two versions of NIPEN: one is a hybrid model of deep learning and probabilistic graphical model, and the other model is a neural tensor model. Our result indicates that NIPEN successfully learns the manifold of the legislative bill’s text, and NIPEN utilizes the learned low-dimensional latent variables to increase the prediction performance of legislators’ votings. Additionally, by virtue of being a domain-rich probabilistic model, NIPEN shows the hidden strength of the legislators’ trust network and their various characteristics on casting votes.

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IJCAI Conference 2016 Conference Paper

Bayesian Nonparametric Collaborative Topic Poisson Factorization for Electronic Health Records-Based Phenotyping

Wonsung Lee
Youngmin Lee
Heeyoung Kim
Il-Chul Moon

Phenotyping with electronic health records (EHR) has received much attention in recent years because the phenotyping opens a new way to discover clinically meaningful insights, such as disease progression and disease subtypes without human supervisions. In spite of its potential benefits, the complex nature of EHR often requires more sophisticated methodologies compared with traditional methods. Previous works on EHR-based phenotyping utilized unsupervised and supervised learning methods separately by independently detecting phenotypes and predicting medical risk scores. To improve EHR-based phenotyping by bridging the separated methods, we present Bayesian nonparametric collaborative topic Poisson factorization (BN-CTPF) that is the first nonparametric content-based Poisson factorization and first application of jointly analyzing the phenotye topics and estimating the individual risk scores. BN-CTPF shows better performances in predicting the risk scores when we compared the model with previous matrix factorization and topic modeling methods including a Poisson factorization and its collaborative extensions. Also, BN-CTPF provides faceted views on the phenotype topics by patients' demographics. Finally, we demonstrate a scalable stochastic variational inference algorithm by applying BN-CTPF to a national-scale EHR dataset.

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