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Differential Privacy in Federated Dynamic Gradient Clip** Based on Gradient Norm
Federal learning achieves privacy preservation by adding noise to gradient. The noise needs to be clipped to prevent excessive noise from...
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State space representation and phase analysis of gradient descent optimizers
Deep learning has achieved good results in the field of image recognition due to the key role of the optimizer in a deep learning network. In this...
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Isogeometric analysis of shear-deformable, in-plane functionally graded microshells by Mindlin’s strain gradient theory
This paper proposes a general strain-gradient and shear-deformable isogeometric microshell formulation based on the complete Mindlin’s form II strain...
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Diabetes Risk Prediction Through Fine-Tuned Gradient Boosting
Diabetes, a chronic metabolic disease with a rising global prevalence, significantly impacts individuals’ health. Diabetes increases a person’s risk...
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Modified strain gradient analysis of the functionally graded triply periodic minimal surface microplate using isogeometric approach
This article aims to study the free vibration, buckling and bending behaviours of the functionally graded triply periodic minimal surface (FG-TPMS)...
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Stochastic variance reduced gradient with hyper-gradient for non-convex large-scale learning
Non-convex optimization, which can better capture the problem structure, has received considerable attention in the applications of machine learning,...
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An Improvised Sentiment Analysis Model on Twitter Data Using Stochastic Gradient Descent (SGD) Optimization Algorithm in Stochastic Gate Neural Network (SGNN)
Sentiment analysis is one of the effective techniques for mining the opinion from shapeless data contains text like review of the products, review of...
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Nonlocal strain gradient analysis of FG GPLRC nanoscale plates based on isogeometric approach
In this paper, a nonlocal strain gradient isogeometric model based on the higher order shear deformation theory for free vibration analysis of...
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Conjugate Gradient Method for finding Optimal Parameters in Linear Regression
Linear regression is one of the most celebrated approaches for modeling the relationship between independent and dependent variables in a prediction...
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Construct a Secure CNN Against Gradient Inversion Attack
Federated learning enables collaborative model training across multiple clients without sharing raw data, adhering to privacy regulations, which...
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A convergence analysis of hybrid gradient projection algorithm for constrained nonlinear equations with applications in compressed sensing
In this paper, we propose a projection-based hybrid spectral gradient algorithm for nonlinear equations with convex constraints, which is based on a...
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Detecting Adversarial Examples via Local Gradient Checking
Deep neural networks (DNNs) are vulnerable to adversarial examples, which may lead to catastrophe in security-critical domains. Numerous detection...
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Systematic Literature Review and Bibliometric Analysis on Addressing the Vanishing Gradient Issue in Deep Neural Networks for Text Data
The feature to learn complex text representations enabled by Deep Neural Networks (DNNs) has revolutionized Natural Language Processing and several...
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Attention-enhanced UNet and gradient boosting decision tree for objective evaluation of fabric pilling grade based on image analysis
Fabric pilling can significantly affect the usability and appearance of fabrics; making the evaluation of pilling grades a crucial aspect of textile...
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Gradient leakage attacks in federated learning
Federated Learning (FL) improves the privacy of local training data by exchanging model updates (e.g., local gradients or updated parameters)....
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Gradient-based elephant herding optimization for cluster analysis
Clustering analysis is essential for obtaining valuable information from a predetermined dataset. However, traditional clustering methods suffer from...
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Gradient-Based Competitive Learning: Theory
Deep learning has been recently used to extract the relevant features for representing input data also in the unsupervised setting. However,...
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Doubly Accelerated Proximal Gradient for Nonnegative Tensor Decomposition
The accelerated proximal gradient (APG) is a classical algorithm for nonnegative tensor decomposition. The APG employs variable extrapolation to...
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SWG: an architecture for sparse weight gradient computation
On-device training for deep neural networks (DNN) has become a trend due to various user preferences and scenarios. The DNN training process consists...
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Stochastic three-term conjugate gradient method with variance technique for non-convex learning
In the training process of machine learning, the minimization of the empirical risk loss function is often used to measure the difference between the...
