Learning from the Hindsight Plan: On Learning from Exact Time-series Data


Learning from the Hindsight Plan: On Learning from Exact Time-series Data – This paper presents a framework for a general framework for learning and reasoning from data that is similar to the stochastic optimization method known as SPM. The framework contains two main parts: learning from data samples and reasoning from time-series data. The learning algorithm is shown to be the simplest and most robust algorithm for learning a given data set. Using the stochastic gradient descent algorithm as an example, the main objective of this method is to approximate the optimal parameter in the stochastic gradient descent algorithm. In this work, the proposed framework is compared to a stochastic optimization method based on Bayesian gradient descent, a variational optimization algorithm, and is shown to be the most robust algorithm that we have found that is also suitable for time-series data. The framework also provides a simple and robust algorithm for Bayesian gradient descent.

Although there are existing state-of-the-art models for EEG prediction, the quality of predictions remains very poor. We propose a method for combining the output of two separate time-domain time-dependent sub-sampling methods with a global filter method to generate a global model of EEG signals. Our objective is to use a global model learned by the two methods to form the local model of EEG signal while preserving its local structure. The learned global model is constructed by learning the time-frequency correspondence of EEG signal in a non-overlapping network. We validate our method on standard clinical EEG datasets consisting of 5-8 individuals. Our method is significantly faster to train on a single EEG dataset compared to the state-of-the-art methods when trained on it over a set of multiple individuals, thus outperforming a standard EEG predictor. We also show that the proposed method can significantly outperform the state-of-the-art method.

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Learning from the Hindsight Plan: On Learning from Exact Time-series Data

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  • Adaptive Neighbors and Neighbors by Nonconvex Surrogate Optimization

    A Gaussian mixture model framework for feature selection of EEGs for narcolepsyAlthough there are existing state-of-the-art models for EEG prediction, the quality of predictions remains very poor. We propose a method for combining the output of two separate time-domain time-dependent sub-sampling methods with a global filter method to generate a global model of EEG signals. Our objective is to use a global model learned by the two methods to form the local model of EEG signal while preserving its local structure. The learned global model is constructed by learning the time-frequency correspondence of EEG signal in a non-overlapping network. We validate our method on standard clinical EEG datasets consisting of 5-8 individuals. Our method is significantly faster to train on a single EEG dataset compared to the state-of-the-art methods when trained on it over a set of multiple individuals, thus outperforming a standard EEG predictor. We also show that the proposed method can significantly outperform the state-of-the-art method.


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