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Results for Machine/Deep Learning /AI




Results for Machine/Deep Learning /AI

Global Optimality in Inductive Matrix Completion
RESEARCH PAPER / Apr 2018 / Machine/Deep Learning /AI
Inductive matrix completion (IMC) is a model for incorporating side information in form of “features” of the row and column entities of an unknown matrix in the matrix completion problem. As side information, features can substantially reduce the number of observed entries required for reconstructing an unknown matrix from its...
CNN-BASED TRANSFORM SYNTAX PREDICTION IN ADAPTIVE MULTIPLE TRANSFORMS FRAMEWORK TO ASSIST ENTROPY CODING IN HEVC
RESEARCH PAPER / Aug 2017 / Machine/Deep Learning /AI, Image Processing, Video Coding
Recent work in video compression has shown that using multiple 2D transforms instead of a single transform in order to de-correlate residuals provides better compression efficiency. These transforms are tested competitively inside a video encoder and the optimal transform is selected based on the Rate Distortion Optimization (RDO) cost. However,...
Inverse Covariance Estimation with Structured Groups
RESEARCH PAPER / Aug 2017 / Machine/Deep Learning /AI
Estimating the inverse covariance matrix of p variables from n observations is challenging when n  p, since the sample covariance matrix is singular and cannot be inverted. A popular solution is to optimize for the `1 penalized estimator; however, this does not incorporate structure domain knowledge and can be...
Goal Directed Inductive Matrix Completion
RESEARCH PAPER / Aug 2017 / Machine/Deep Learning /AI
Matrix completion (MC) with additional information has found wide applicability in several machine learning applications. Among algorithms for solving such problems, Inductive Matrix Completion(IMC) has drawn a considerable amount of attention, not only for its well established theoretical guarantees but also for its superior performance in various real-world applications. However,...

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