A Note on the SP Inference for Large-scale Covariate Regression


A Note on the SP Inference for Large-scale Covariate Regression – We solve large-scale regression problems for which the data are represented by a set of linear functions in a non-convex way. By using nonconvex functions, we also can approximate the sparsity problem. A practical algorithm to approximate a polynomial function is presented. The algorithm is proved to be significantly faster; it is shown to be efficient in practice.

Despite the huge growth in renewable energy generation in the last two decades, current solar thermal generation is still the world-leading renewable energy generation. Many problems associated with existing solar thermal generation have to be addressed to make the situation more beneficial, since it is extremely difficult to forecast a temperature change of the sun for the whole solar system, especially for the first few years. In this work, we propose a novel online strategy to improve the energy efficiency of solar thermal generation. The proposed strategy is based on an algorithm called Temporal Sorting, i.e., the task of locating the keypoints in an optimal sequence of events, i.e., the temporal order which occurs when each event is in range of two adjacent events. The keypoint is the location of the most important event in the sequence of events, which we call the Temporal Sorting algorithm. We demonstrate how the Temporal Sorting is a useful tool for a specific type of solar thermal generation, namely, a multi-temperature solar thermal system.

Deep Reinforcement Learning with Temporal Algorithm and Trace Distance

Graph Deconvolution Methods for Improved Generative Modeling

A Note on the SP Inference for Large-scale Covariate Regression

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  • A Deep Knowledge Based Approach to Safely Embedding Neural Networks

    An Online Strategy to Improve Energy Efficiency through OptimisationDespite the huge growth in renewable energy generation in the last two decades, current solar thermal generation is still the world-leading renewable energy generation. Many problems associated with existing solar thermal generation have to be addressed to make the situation more beneficial, since it is extremely difficult to forecast a temperature change of the sun for the whole solar system, especially for the first few years. In this work, we propose a novel online strategy to improve the energy efficiency of solar thermal generation. The proposed strategy is based on an algorithm called Temporal Sorting, i.e., the task of locating the keypoints in an optimal sequence of events, i.e., the temporal order which occurs when each event is in range of two adjacent events. The keypoint is the location of the most important event in the sequence of events, which we call the Temporal Sorting algorithm. We demonstrate how the Temporal Sorting is a useful tool for a specific type of solar thermal generation, namely, a multi-temperature solar thermal system.


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