Tuning for Semi-Supervised Learning via Clustering and Sparse Lifting


Tuning for Semi-Supervised Learning via Clustering and Sparse Lifting – Semi-supervised learning systems employ the nonlinearity of the inputs to train the network to make more observations per second. However, it is generally not known what is the optimal value of these representations as a function of the training set. We propose a non-linear learning rule to estimate the true values of the hidden representations, and show that this strategy, called learning the value of the noise by the nonlinearity, is accurate enough to achieve good results.

Many recent methods using convolutional neural networks to solve optimization problems in an unsupervised manner based on random forests were shown to have good performance. In this paper, we study the performance of these state-of-the-art method on two large-scale benchmark benchmarks involving various supervised learning models: a novel dataset from the ROCA workshop (where a variety of algorithms, such as Genetic Algorithms, have been proposed) and a new dataset from the ILSVRC 2016 workshop on unsupervised learning.

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Tuning for Semi-Supervised Learning via Clustering and Sparse Lifting

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  • Explanation-based analysis of taxonomic information in taxonomical text

    Convex Optimization for Large Scale Multi-view Super-resolutionMany recent methods using convolutional neural networks to solve optimization problems in an unsupervised manner based on random forests were shown to have good performance. In this paper, we study the performance of these state-of-the-art method on two large-scale benchmark benchmarks involving various supervised learning models: a novel dataset from the ROCA workshop (where a variety of algorithms, such as Genetic Algorithms, have been proposed) and a new dataset from the ILSVRC 2016 workshop on unsupervised learning.


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