This article focuses on gradient-based backpropagation algorithms that use either a common adaptive learning rate for all weights or an individual adaptive learning rate for each weight and apply the Goldstein/Armijo line search. The learning-rate adaptation is based on descent techniques and estimates of the local Lipschitz constant that are obtained without additional error function and gradi...

the purpose of this study was to investigate the mediating role of learning goal orientation at the individual, group and organizational level in the relationship of transformational leadership with adaptive performance. the sample consisted of 175 employees of a service organization. fitness of the proposed model and indirect effects was examined through structural equation modeling and bootst...

Adaptive Optimization Hyperparameter tuning is a big cost of deep learning. Momentum: a key hyperparameter to SGD and variants. Adaptive methods, e.g. Adam1, don’t tune momentum. YellowFin optimizer • Based on the robustness properties of momentum. • Auto-tuning of momentum and learning rate in SGD. • Closed-loop momentum control for async. training. Experiments ResNet and LSTM YellowFin runs w...

In the past three decades, the use of smart methods in medical diagnostic systems has attracted the attention of many researchers. However, no smart activity has been provided in the field of medical image processing for diagnosis of bladder cancer through cystoscopy images despite the high prevalence in the world. In this paper, a multilayer neural network was applied to clas...

Abstract Various works have been published around the optimization of Neural Networks that emphasize significance learning rate. In this study we analyze need for a different treatment each layer and how affects training. We propose novel technique, called AdaLip, utilizes an estimation Lipschitz constant gradients in order to construct adaptive rate per can work on top already existing optimiz...

one of the problems associated with adaptive fir filters in the identification of systems with long impulse responses, is their excessive computational complexity. recently a new kind of adaptive filters, based on three-level clipping of the input signal has been presented for reduction of their computational complexity. in this paper, a theoretical analysis of the steady-state misalignment of ...

In this paper, we present an improved version of the online phase-space learning algorithm of Tsung and Cottrell (1995), called ARTISTE (Autonomous Real-TIme Selection of Training Examples). The new version’s advantages derive from an online adaptive learning rate that depends on the error. We demonstrate the algorithm’s efficacy on two problems: learning a pair of sine waves offset by 901 and ...