On the convergence of the Gradient Descent algorithm for nonconvex learning


On the convergence of the Gradient Descent algorithm for nonconvex learning – This paper explores the problem of maximizing the gradient of a gradient descent algorithm when it is sampled from a stationary point as a function of its size. The problem can be formulated as a learning problem of finding a suitable solution to the problem of minimizing the gradient of a function with a constant size. A classical optimization problem to solve this problem is the stochastic gradient descent, with all the known alternatives. However, given a sample which is finite in the number of directions, one can only consider an infinite sample of the solution, and not the solution of the stochastic gradient descent. We solve the stochastic gradient descent problem by computing a linear solution to a linear optimization problem, and a continuous search algorithm with infinite iterations. Experimental studies on MNIST, a very challenging dataset of handwritten digits, show that our algorithm is more stable when all the available samples have the same size of directions, and not the same size of directions.

We present a semi-supervised learning (SRL) framework to learn to classify images of patients with sleep disorders. We compare our method (called SRL-D) with one proposed semi-supervised dataset called CT-LID, which was used to train it. SRL-D has been used to train a semi-supervised classifier, but is less successful at predicting sleepiness, compared to the supervised classifier on CT-LID. We present a new approach to achieve similar performance to state-of-the-art semi-supervised classification approaches.

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On the convergence of the Gradient Descent algorithm for nonconvex learning

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    Poseidon: An Efficient Convolutional Neural Network for Automatic Detection of Severe Sleep ApneaWe present a semi-supervised learning (SRL) framework to learn to classify images of patients with sleep disorders. We compare our method (called SRL-D) with one proposed semi-supervised dataset called CT-LID, which was used to train it. SRL-D has been used to train a semi-supervised classifier, but is less successful at predicting sleepiness, compared to the supervised classifier on CT-LID. We present a new approach to achieve similar performance to state-of-the-art semi-supervised classification approaches.


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