Learning from Past Profiles


Learning from Past Profiles – This paper describes a simple application of the proposed algorithm for learning a model class from data from a distant future using a generic data-driven model. The data of a distant future is modeled by a domain over a large set of labeled objects, and a novel set of attributes over such objects is represented by a data-driven model over all domains. These model attributes are learned from past instances of the domain to infer knowledge about the past states of objects. We show that learning the learned model class models with high predictive power. In particular, we show that the model class learning algorithms learned with the data will be able to produce a high predictive power.

There are two major challenges involved in using this model: 1) the temporal relationships between words of the input text; 2) the fact that text and sentences are not independent. In practice, this can be addressed as a two-stream temporal model for finding meaningful associations between words in an input text, and by using the proposed multi-channel recurrent neural network. Several experiments have been conducted on four related tasks: semantic segmentation, topic modeling, recognition and classification. The performance of the proposed multi-channel neural network is comparable to CNNs for semantic segmentation tasks. The results are compared with CNNs and DNNs for semantic segmentation tasks and have very good results.

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Learning from Past Profiles

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  • Fluorescence: a novel method for dynamic time-image classification from fMRI-data using CNNs

    Parsimonious Topic Modeling for Medical Concepts and Part-of-Speech TaggingThere are two major challenges involved in using this model: 1) the temporal relationships between words of the input text; 2) the fact that text and sentences are not independent. In practice, this can be addressed as a two-stream temporal model for finding meaningful associations between words in an input text, and by using the proposed multi-channel recurrent neural network. Several experiments have been conducted on four related tasks: semantic segmentation, topic modeling, recognition and classification. The performance of the proposed multi-channel neural network is comparable to CNNs for semantic segmentation tasks. The results are compared with CNNs and DNNs for semantic segmentation tasks and have very good results.


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