Deep Predicting Adolescent Suicide Attempts by Exploiting Drug-Drug Interactions


Deep Predicting Adolescent Suicide Attempts by Exploiting Drug-Drug Interactions – We propose a novel approach for automatic diagnosis of bipolar disorders based on the notion of a bipolar diagnosis. The method relies on a convolutional network to learn a multispectral model to predict whether a patient will show bipolar disorder based on data with three sources or not. The multispectral network model can perform a binary decision (to classify the patient as bipolar, or to classify a drug as non-bipolar) and can be implemented in an adversarial fashion. Experiments on both synthetic and real data demonstrate the superiority of the proposed method compared to state-of-the-art baselines.

We propose to model the causal relationships among events by using a framework with an adaptive structure. We first show the model is capable of predicting the outcome of the event based on the observed outcome. We then present a new nonparametric model to capture the causal relationship among multiple events based on the observed events. We demonstrate the model and demonstrate the effectiveness of the inference in several simulated and real events.

There are very few algorithms for learning to compose language and to translate an artificial language. Recent methods have been developed to learn language learning without using artificial language. While neural networks with language learning algorithms were successful in many tasks, they are limited in how to achieve language learning algorithms that perform effectively on language-independent tasks. In this work, we analyze performance of language learning algorithms in various language domains. We provide an analysis of a few language learning algorithms that have been published, and of various language learning algorithms. The analysis reveals performance of the language learning algorithmic framework, and provide insight into how language learning algorithms are able to achieve good performance. The results also suggest a methodology for the implementation of language learning algorithms for some languages and domains.

Efficient Online Sufficient Statistics for Transfer in Machine Learning with Deep Learning

Learning Nonlinear Process Models for Deep Neural Networks

Deep Predicting Adolescent Suicide Attempts by Exploiting Drug-Drug Interactions

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  • A Generalized Baire Gradient Method for Gaussian Graphical Models

    Learning to Compose Domain-Specific LanguagesThere are very few algorithms for learning to compose language and to translate an artificial language. Recent methods have been developed to learn language learning without using artificial language. While neural networks with language learning algorithms were successful in many tasks, they are limited in how to achieve language learning algorithms that perform effectively on language-independent tasks. In this work, we analyze performance of language learning algorithms in various language domains. We provide an analysis of a few language learning algorithms that have been published, and of various language learning algorithms. The analysis reveals performance of the language learning algorithmic framework, and provide insight into how language learning algorithms are able to achieve good performance. The results also suggest a methodology for the implementation of language learning algorithms for some languages and domains.


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