Interpretability in Machine Learning

Interpretability in Machine Learning – We present in this paper a statistical procedure that gives the maximum accuracy on the posterior of all the possible outputs of a given model with a fixed amount of data. The procedure is illustrated using a standard dataset, namely the dataset generated with a model with a certain number of parameters. The procedure is illustrated with a model with certain number of parameters.

We propose a deep-learning method to predict the action using contextual MAPs for action prediction in real-time. The state of the art works use a mixture of the following two strategies. First the action prediction is used to predict whether two events should be considered as a single event and by what order they should be considered. Second the Mapped Contextual Mapping is used to predict whether the action should be considered as a chain event or a sequence of events. Finally the Contextual Mapping for the action prediction is used to predict the sequence of events from the contextual data in order to predict the action’s outcome. Compared to state-of-the-art deep learning methods, our method outperforms them in terms of accuracy and speed.

Learning Data Representations for Video Classification with Convolutional Neural Networks

Deep learning for the classification of emotionally charged events

Interpretability in Machine Learning

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  • Sparse Representation by Partial Matching

    End-to-End Action Detection with Dynamic Contextual MappingWe propose a deep-learning method to predict the action using contextual MAPs for action prediction in real-time. The state of the art works use a mixture of the following two strategies. First the action prediction is used to predict whether two events should be considered as a single event and by what order they should be considered. Second the Mapped Contextual Mapping is used to predict whether the action should be considered as a chain event or a sequence of events. Finally the Contextual Mapping for the action prediction is used to predict the sequence of events from the contextual data in order to predict the action’s outcome. Compared to state-of-the-art deep learning methods, our method outperforms them in terms of accuracy and speed.


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