2,905 research outputs found
Adversarial Dropout for Supervised and Semi-supervised Learning
Recently, the training with adversarial examples, which are generated by
adding a small but worst-case perturbation on input examples, has been proved
to improve generalization performance of neural networks. In contrast to the
individually biased inputs to enhance the generality, this paper introduces
adversarial dropout, which is a minimal set of dropouts that maximize the
divergence between the outputs from the network with the dropouts and the
training supervisions. The identified adversarial dropout are used to
reconfigure the neural network to train, and we demonstrated that training on
the reconfigured sub-network improves the generalization performance of
supervised and semi-supervised learning tasks on MNIST and CIFAR-10. We
analyzed the trained model to reason the performance improvement, and we found
that adversarial dropout increases the sparsity of neural networks more than
the standard dropout does.Comment: submitted to AAAI-1
Hierarchical Context enabled Recurrent Neural Network for Recommendation
A long user history inevitably reflects the transitions of personal interests
over time. The analyses on the user history require the robust sequential model
to anticipate the transitions and the decays of user interests. The user
history is often modeled by various RNN structures, but the RNN structures in
the recommendation system still suffer from the long-term dependency and the
interest drifts. To resolve these challenges, we suggest HCRNN with three
hierarchical contexts of the global, the local, and the temporary interests.
This structure is designed to withhold the global long-term interest of users,
to reflect the local sub-sequence interests, and to attend the temporary
interests of each transition. Besides, we propose a hierarchical context-based
gate structure to incorporate our \textit{interest drift assumption}. As we
suggest a new RNN structure, we support HCRNN with a complementary
\textit{bi-channel attention} structure to utilize hierarchical context. We
experimented the suggested structure on the sequential recommendation tasks
with CiteULike, MovieLens, and LastFM, and our model showed the best
performances in the sequential recommendations
Improvisation of classification performance based on feature optimization for differentiation of Parkinson’s disease from other neurological diseases using gait characteristics
Most neurological disorders that include Parkinson’s disease (PD) as well as other neurological diseases such as Amyotrophic Lateral Sclerosis (ALS) and Huntington’s disease (HD) have some common abnormalities regarding the movement, vocal, and cognitive behaviors of sufferers. Variations in the manifestation of these types of abnormality help distinguish one disorder from another. In this study, differentiation was performed based on the gait characteristics of patients afflicted by different neurological disorders. In the recent past, many researchers have applied different machine learning and feature selection techniques to the classification of different groups of patients based on common abnormalities. However, in an era of modernization where the focus is on timely low-cost automatization and pattern recognition, such techniques require improvisation to provide high performance. We attempted to improve the performance of such techniques using different feature optimization methods, such as a genetic algorithm (GA) and principal component analysis (PCA), and applying different classification approaches, i.e., linear, nonlinear, and probabilistic classifiers. In this study, gait dynamics data of patients suffering with PD, ALS, and HD were collated from a public database, and a binary classification approach was used by taking PD as one group and adopting ALS+HD as another group. Performance comparison was achieved using different classification techniques that incorporated optimized feature sets obtained from GA and PCA. In comparison with other classifiers using different feature sets, the highest accuracy (97.87%) was obtained using random forest combined with GA-based feature sets. The results provide evidence that could assist medical practitioners in differentiating PD from other neurological diseases using gait characteristics
Adversarial Dropout for Recurrent Neural Networks
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.Comment: published in AAAI1
Korea’s technical assistance for better governance
노트 : - Paper for International Conference on U.S.-Korea Dialogue on Strategies for Effective Development Cooperation
- Organized by Asia Foundation October 17-18, 2011 Seoul, Korea
행사명 : International Conference on U.S.-Korea Dialogue on Strategies for Effective Development Cooperatio
Transcanal Endoscopic Ear Surgery for Congenital Cholesteatoma
Objectives As endoscopic instrumentation, techniques and knowledges have significantly improved recently, endoscopic ear surgery has become increasingly popular. Transcanal endoscopic ear surgery (TEES) can provide better visualization of hidden areas in the middle ear cavity during congenital cholesteatoma removal. We aimed to describe outcomes for TEES for congenital cholesteatoma in a pediatric population. Methods Twenty-five children (age, 17 months to 9 years) with congenital cholesteatoma confined to the middle ear underwent TEES by an experienced surgeon; 13 children had been classified as Potsic stage I, seven as stage II, and five as stage III. The mean follow-up period was 24 months. Recurrence of congenital cholesteatoma and surgical complication was observed. Results Congenital cholesteatoma can be removed successfully via transcanal endoscopic approach in all patients, and no surgical complications occurred; only one patient with a stage II cholesteatoma showed recurrence during the follow-up visit, and the patient underwent revision surgery. The other patients underwent one-stage operations and showed no cholesteatoma recurrence at their last visits. Two patients underwent second-stage ossicular reconstruction. Conclusion Although the follow-up period and number of patients were limited, pediatric congenital cholesteatoma limited to the middle ear cavity could be safely and effectively removed using TEES
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