Fast Learning of Multi-Task Networks for Predictive Modeling


Fast Learning of Multi-Task Networks for Predictive Modeling – In this paper we propose a general method, named Context-aware Temporal Learning (CTL), for extracting long-term dependencies across subnetworks from multi-task networks (MTNs) as well as in particular from multi-task networks. To understand why it is useful for this task, we examine the impact of two factors: (1) the structure of the MTN and the performance of the model; and (2) the number of training blocks. The results indicate that in this setting, we can achieve state-of-the-art performance, despite only using two large MTNs.

Automated energy consumption prediction has recently received renewed attention in many fields ranging from machine learning to medicine. Though this is an important task, it is challenging because most existing methodologies have too much supervision to handle the task. To tackle this, we propose a nonparametric model of energy consumption forecast in order to learn prediction from data. We exploit a general framework, called Deep Belief Propagation (DBP), for this purpose. Using a combination of Bayesian Bayesian inference and Bayesian network training, we construct a fully Bayesian model for prediction of energy consumption. Extensive experiments validate the efficacy and efficiency of the proposed approach and show results on a variety of commodity metrics.

Pseudo Generative Adversarial Networks: Learning Conditional Gradient and Less-Predictive Parameter

Lifted Bayesian Learning in Dynamic Environments

Fast Learning of Multi-Task Networks for Predictive Modeling

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  • A Probabilistic Graphical Model for Multi-Relational Data Analysis Under Data Associated Condition Conditions

    A Bayesian nonparametric neural network approach to predict oil price volatility predictionAutomated energy consumption prediction has recently received renewed attention in many fields ranging from machine learning to medicine. Though this is an important task, it is challenging because most existing methodologies have too much supervision to handle the task. To tackle this, we propose a nonparametric model of energy consumption forecast in order to learn prediction from data. We exploit a general framework, called Deep Belief Propagation (DBP), for this purpose. Using a combination of Bayesian Bayesian inference and Bayesian network training, we construct a fully Bayesian model for prediction of energy consumption. Extensive experiments validate the efficacy and efficiency of the proposed approach and show results on a variety of commodity metrics.


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