Results 1 to 10 of about 3,718,623 (231)
A modified Adam algorithm for deep neural network optimization
Neural Computing and Applications, 2023 Deep Neural Networks (DNNs) are widely regarded as the most effective learning tool for dealing with large datasets, and they have been successfully used in thousands of applications in a variety of fields. Based on these large datasets, they are trained
Amany M Sarhan, M Arafa
exaly +2 more sources
An Effective Optimization Method for Machine Learning Based on ADAM
Applied Sciences, 2020 A machine is taught by finding the minimum value of the cost function which is induced by learning data. Unfortunately, as the amount of learning increases, the non-liner activation function in the artificial neural network (ANN), the complexity of the ...
Dokkyun Yi, Jaehyun Ahn, Sangmin Ji
doaj +2 more sources
Efficient-Adam: Communication-Efficient Distributed Adam [PDF]
IEEE Transactions on Signal Processing, 2023 Distributed adaptive stochastic gradient methods have been widely used for large-scale nonconvex optimization, such as training deep learning models. However, their communication complexity on finding $\varepsilon$-stationary points has rarely been analyzed in the nonconvex setting.
Congliang Chen +3 more
openaire +3 more sources
Molecular Aspects of Medicine, 2008 The ADAMs (a disintegrin and metalloproteinase) are a fascinating family of transmembrane and secreted proteins with important roles in regulating cell phenotype via their effects on cell adhesion, migration, proteolysis and signalling. Though all ADAMs contain metalloproteinase domains, in humans only 13 of the 21 genes in the family encode functional
Dylan R Edwards, Caroline J Pennington
exaly +4 more sources
Regulation of Fibrotic Processes in the Liver by ADAM Proteases
Cells, 2019 Fibrosis in the liver is mainly associated with the activation of hepatic stellate cells (HSCs). Both activation and clearance of HSCs can be mediated by ligand−receptor interactions.
Dirk Schmidt-Arras, Stefan Rose-John
exaly +3 more sources
Anthropological lexicon of the Septuagint: turning to spiritualization and rethinking of traditional hebrew semantics [PDF]
Вестник Свято-Филаретовского института, 2022 The article is dedicated to the comparison of anthropological models of the Hebrew Bible and the Greek Bible (Septuagint). Through lexical and theological analysis, the article reveals the correlation of typical anthropological ideas of the Hebrew Bible ...
Zykov Vyacheslav
doaj +1 more source
Noise Is Not the Main Factor Behind the Gap Between SGD and Adam on Transformers, but Sign Descent Might Be [PDF]
International Conference on Learning Representations, 2023 The success of the Adam optimizer on a wide array of architectures has made it the default in settings where stochastic gradient descent (SGD) performs poorly.
Frederik Kunstner +3 more
semanticscholar +1 more source
Convergence of Adam Under Relaxed Assumptions [PDF]
Neural Information Processing Systems, 2023 In this paper, we provide a rigorous proof of convergence of the Adaptive Moment Estimate (Adam) algorithm for a wide class of optimization objectives.
Haochuan Li, A. Jadbabaie, A. Rakhlin
semanticscholar +1 more source
The buffered optimization methods for online transfer function identification employed on DEAP actuator [PDF]
Archives of Control Sciences, 2023 Identification plays an important role in relation to control objects and processes as it enables the control system to be properly tuned. The identification methods described in this paper use the Stochastic Gradient Descent algorithms, which have so ...
Jakub Bernat, Jakub Kołota
doaj +1 more source
Toward Understanding Why Adam Converges Faster Than SGD for Transformers [PDF]
arXiv.org, 2023 While stochastic gradient descent (SGD) is still the most popular optimization algorithm in deep learning, adaptive algorithms such as Adam have established empirical advantages over SGD in some deep learning applications such as training transformers ...
Yan Pan
semanticscholar +1 more source