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Convergence of Adam for non-convex objectives: relaxed hyperparameters and non-ergodic case
Adam is a commonly used stochastic optimization algorithm in machine learning. However, its convergence is still not fully understood, especially in...
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AMAdam: adaptive modifier of Adam method
This paper presents AMAdam, an innovative adaptive modifier gradient descent optimization algorithm that aims to overcome the challenges faced by...
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A modified Adam algorithm for deep neural network optimization
Deep Neural Networks (DNNs) are widely regarded as the most effective learning tool for dealing with large datasets, and they have been successfully...
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Theoretical analysis of Adam using hyperparameters close to one without Lipschitz smoothness
Convergence and convergence rate analyses of adaptive methods, such as Adaptive Moment Estimation (Adam) and its variants, have been widely studied...
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BAAO: Bayesian and Adam optimizer for fault prediction in self-driving software systems using deep learning-based hyperparameter tuning
Deep learning (DL) is crucial for advancing autonomous driving systems. The Basic requirement for successful and robust prediction of fault in...
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Virtual Machine Placement Using Adam White Shark Optimization Algorithm in Cloud Computing
The increasing demand for virtual machine (VM) request is caused due to the increasing number of users. Hence, the VM placement is considered as a...
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ADAM-DPGAN: a differential private mechanism for generative adversarial network
Privacy preserving data release is a major concern of many data mining applications. Using Generative Adversarial Networks (GANs) to generate an...
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Analysis of traffic flow prediction from spatial-temporal data using hybrid GSA-Adam optimizer based LSTM network for intelligent transport system
In recent decades, the intelligent transportation system (ITS) has gotten extensive consideration, because of greater levels of popularity for road...
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ADAM optimised human speech emotion recogniser based on statistical information distribution of chroma, MFCC, and MBSE features
The textual or display-based control paradigm in human–computer interaction (HCI) has changed in favor of more natural control modalities like voice...
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Evaluating Adan vs. Adam: An Analysis of Optimizer Performance in Deep Learning
Choosing a suitable optimization algorithm in deep learning is essential for effective model development as it significantly influences convergence...
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On Suppressing Range of Adaptive Stepsizes of Adam to Improve Generalisation Performance
A number of recent adaptive optimizers improve the generalisation performance of Adam by essentially reducing the variance of adaptive stepsizes to...
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Adam Lyrebird Optimization-Based DLSTM for Solar Irradiance Prediction Using Time Series Data
In a variety of fields including climatology, energy, and engineering, precise solar irradiance measurement is crucial. For most studies, the model...
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ADAM: A Prototype of Hierarchical Neuro-Symbolic AGI
Intelligent agents are characterized primarily by their far-sighted expedient behavior. We present a working prototype of an intelligent agent (ADAM)...
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ADAM: Automatic Detection of Android Malware
The popularity of the Android operating system has been rising ever since its initial release in 2008. This is due to two major reasons. The first is...
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A Time Series Forecasting Method Using DBN and Adam Optimization
Deep Belief Net (DBN) was applied to the field of time series forecasting in our early works. In this paper, we propose to adopt Adaptive Moment...
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FedTA: Locally-Differential Federated Learning with Top-k Mechanism and Adam Optimization
With the explosive development of fields including big data and cloud computing, it has become a global trend for the public to place a premium on...
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Could Adam Smith Live in a Smart City?
In this paper we look at how Smart Cities can accelerate trends in society initiated by the information economy that run counter to traditional...
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Evaluating Performance of Adam Optimization by Proposing Energy Index
The adjustment of learning rate ( \(\eta \) ),...
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RNN / LSTM with modified Adam optimizer in deep learning approach for automobile spare parts demand forecasting
The spare parts demand forecasting is very much essential for the organizations to minimize the cost and prevent the stock outs. The demand of spare...
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Nesterov Adam Iterative Fast Gradient Method for Adversarial Attacks
Deep Neural Networks (DNNs) are vulnerable to adversarial examples that mislead DNNs with imperceptible perturbations. Existing adversarial attacks...