May 24, 2019 · This paper introduces a new deep learning approach for approximating a nearest matrix with more efficient runtime complexity than existing algorithms.
This paper introduces a new deep learning approach for approximating a nearest matrix with more efficient runtime complexity than existing algorithms and ...
This paper introduces a new deep learning approach for approximating a nearest matrix with more efficient runtime complexity than existing algorithms. We have ...
This paper introduces a new deep learning approach for approximating a nearest matrix with more efficient runtime complexity than existing algorithms. We have ...
Abstract. Metric nearness refers to the problem of optimally restoring metric properties to distance measurements that happen to be nonmetric due to ...
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This paper presents the Metric Nearness Prob- lem: Given a dissimilarity matrix, find the nearest matrix of distances that satisfy the triangle inequalities.
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Jan 19, 2019 · Deep NN basically do have the ability to store the training data. In that case, when predicting on new data you are basically doing neighbor ...
Missing: Nearness | Show results with:Nearness
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Jun 26, 2023 · The metric nearness model computes the best approximation of the matrix from a set of valid distance metrics.
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Feb 28, 2022 · In this paper, we propose a novel method to extend ANN search to arbitrary matching functions, e.g., a deep neural network. Our main idea is to ...
This thesis addresses some fundamental problems in data mining and machine learning that may be cast as matrix nearness problems.
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