user: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

Q: Knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space. Existing methods, e.g., TransE and TransH, learn embedding representation by defining a global margin-based loss function over the data. However, the optimal loss function is determined during experiments whose parameters are examined among a closed set of candidates. Moreover, embeddings over two knowledge graphs with different entities and relations share the same set of candidate loss functions, ignoring the locality of both graphs. This leads to the limited performance of embedding related applications. In this paper, we propose a locally adaptive translation method for knowledge graph embedding, called TransA, to find the optimal loss function by adaptively determining its margin over different knowledge graphs. Experiments on two benchmark data sets demonstrate the superiority of the proposed method, as compared to the-state-of-the-art ones.

A: Locally Adaptive Translation for Knowledge Graph Embedding
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Q: The utility of Twitter data as a medium to support populationlevel mental health monitoring is not well understood. In an effort to better understand the predictive power of supervised machine learning classifiers and the influence of feature sets for efficiently classifying depression-related tweets on a large-scale, we conducted two feature study experiments. In the first experiment, we assessed the contribution of feature groups such as lexical information (e.g., unigrams) and emotions (e.g., strongly negative) using a feature ablation study. In the second experiment, we determined the percentile of top ranked features that produced the optimal classification performance by applying a three-step feature elimination approach. In the first experiment, we observed that lexical features are critical for identifying depressive symptoms, specifically for depressed mood (-35 points) and for disturbed sleep (-43 points). In the second experiment, we observed that the optimal F1-score performance of top ranked features in percentiles variably ranged across classes e.g., fatigue or loss of energy (5th percentile, 288 features) to depressed mood (55th percentile, 3,168 features) suggesting there is no consistent count of features for predicting depressive-related tweets. We conclude that simple lexical features and reduced feature sets can produce comparable results to larger feature sets.

A: Feature Studies to Inform the Classification of Depressive Symptoms from Twitter Data for Population Health
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Q: Recent work (Bengio et al., 2013) has shown how Denoising Auto-Encoders(DAE) become generative models as a density estimator. However, in practice, the framework suffers from a mixing problem in the MCMC sampling process and no direct method to estimate the test loglikelihood. We consider a directed model with an stochastic identity mapping (simple corruption process) as an inference model and a DAE as a generative model. By cascading these models, we propose Cascading Denoising AutoEncoders(CDAE) which can generate samples of data distribution from tractable prior distribution under the assumption that probabilistic distribution of corrupted data approaches tractable prior distribution as the level of corruption increases. This work tries to answer two questions. On the one hand, can deep directed models be successfully trained without intractable posterior inference and difficult optimization of very deep neural networks in inference and generative models? These are unavoidable when recent successful directed model like VAE (Kingma & Welling, 2014) is trained on complex dataset like real images. On the other hand, can DAEs get clean samples of data distribution from heavily corrupted samples which can be considered of tractable prior distribution far from data manifold? so-called global denoising scheme. Our results show positive responses of these questions and this work can provide fairly simple framework for generative models of very complex dataset. Proceedings of the 32 International Conference on Machine Learning, Lille, France, 2015. JMLR: W&CP volume 37. Copyright 2015 by the author(s).

A:

assistant: Cascading Denoising Auto-Encoder as a Deep Directed Generative Model
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