An Inertial Newton Algorithm for Deep Learning with Convergence Guarantees

It is based on alternating a quasi-Newton gradient descent for learn- ing the kernel weights and a classical SVM solver for the sample weights; (2) We discuss about convexity for

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An algorithm for generating invisible data poisoning using adversarial noise that breaks image classification deep

Local learning algorithms can require a number of training examples exponential in the input dimension to obtain a given generalization error.... Curse

In doing so, we put to the fore inertial proximal algorithms that converge for general monotone inclusions, and which, in the case of convex minimization, give fast convergence rates

In this paper, we propose an alternative strategy to stochastic gradient algorithms, in the spirit of the Newton algorithm, in the sense that the step sequence of stochastic

Recent works have applied Deep learning techniques to Knowledge tracing (Deep Knowledge Tracing – DKT) in order to model single student’s behavior during the time, in terms of

In this paper we study the convergence properties of the Nesterov’s family of inertial schemes which is a specific case of inertial Gradient Descent algorithm in the context of a