The aim of this study was to examine the effects of online learning self-regulation on learning outcomes during the COVID-19 pandemic lockdown among university college students. Quantitative k-means cluster analysis was used to examine the relationsh...
Active learning strategies respond to the costly labeling task in a supervised classification by selecting the most useful unlabeled examples in training a predictive model. Many conventional active learning algorithms focus on refining the decision...
Gradient Learning (GL), aiming to estimate the gradient of target function, has attracted much attention in variable selection problems due to its mild structure requirements and wide applicability. Despite rapid progress, the majority of the existin...
Gradient descent method is an essential algorithm for learning of neural networks. Among diverse variations of gradient descent method that have been developed for accelerating learning speed, the natural gradient learning is based on the theory of i...
As a new machine learning technique, federated learning has received more attention in recent years, which enables decentralized model training across data silos or edge intelligent devices in the Internet of Things without exchanging local raw data....
Data heterogeneity may significantly deteriorate the performance of federated learning since the client’s data distribution is divergent. To mitigate this issue, an effective method is to partition these clients into suitable clusters. However,...
In the age of artificial intelligence, the best approach to handling huge amounts of data is a tremendously motivating and hard problem. Among machine learning models, stochastic gradient descent (SGD) is not only simple but also very effective. This...
Adaptive gradient descent methods such as Adam, RMSprop, and AdaGrad achieve great success in training deep learning models. These methods adaptively change the learning rates, resulting in a faster convergence speed. Recent studies have shown their...
In this paper, we propose the gradient-oriented prioritization meta-learning (GOPML) algorithm, a new approach for few-shot fault diagnosis in industrial systems. The GOPML algorithm utilizes gradient information to prioritize tasks, aiming to improv...
Machine learning has experienced great success in many applications. Precipitation is a hard meteorological variable to predict, but it has a strong impact on society. Here, a machine-learning technique—a formulation of gradient-boosted trees&m...
In this paper, efficient gradient updating strategies are developed for the federated learning when distributed clients are connected to the server via a wireless backhaul link. Specifically, a common convolutional neural network (CNN) module is shar...
Exchanging gradient is a widely used method in modern multinode machine learning system (e.g., distributed training, Federated Learning). Gradients and weights of model has been presumed to be safe to delivery. However, some studies have shown that g...
In this paper, we propose a novel multi-task learning (MTL) strategy from the gradient optimization view which enables automatically learning the optimal gradient from different tasks. In contrast with current multi-task learning methods which rely o...
Inspired by the recent success of the proximal gradient method (PGM) and recent efforts to develop an inertial algorithm, we propose an inertial PGM (IPGM) for convolutional dictionary learning (CDL) by jointly optimizing both an
Artificial intelligence (AI) technology has advanced significantly, now capable of performing tasks previously believed to be exclusive to skilled humans. However, AI models, in contrast to humans who can develop skills with relatively less data, oft...
As programming education becomes increasingly complex, grading student code has become a challenging task. Traditional methods, such as dynamic and static analysis, offer foundational approaches but often fail to provide granular insights, leading to...
Since cryptocurrencies are among the most extensively traded financial instruments globally, predicting their price has become a crucial topic for investors. Our dataset, which includes fluctuations in Bitcoin’s hourly prices from 15 May 2018 t...
Federated learning (FL) has emerged as an important distributed machine learning paradigm allowing many users to train a model together without sharing raw data. However, FL’s decentralized design makes it very susceptible to both Byzantine and...
Visually rich document understanding (VrDU) models rely on tightly coupled textual, layout, and visual representations. In real-world deployments, these models must continuously adapt to new document domains over time. However, naïve sequential...
Human pose estimation is an important downstream task in computer vision, with significant applications in action recognition and virtual reality. However, data collected in a decentralized manner often exhibit non-independent and identically distrib...
This paper develops a rigorous operator-theoretic framework for learning-augmented quasi-gradient methods in constrained optimization. We consider the minimization of an objective function over a closed convex feasible set, where feasibility is enfor...
Gradient accumulation enables training large-scale deep learning models under GPU memory constraints by aggregating gradients across multiple microbatches before parameter updates. Standard gradient accumulation treats all microbatches uniformly thro...
Offline reinforcement learning aims to learn effective policies from fixed datasets without further interaction with the environment. However, limited behavior-policy coverage can lead to value-estimation errors and unreliable policy improvement, par...
In this paper, we test and evaluate policy gradient reinforcement learning for automated blood glucose control in patients with Type 1 Diabetes Mellitus. Recent research has shown that reinforcement learning is a promising approach to accommodate the...
In this paper, we propose an environment perception framework for autonomous driving using state representation learning (SRL). Unlike existing Q-learning based methods for efficient environment perception and object detection, our proposed method ta...
This manuscript introduces a computational approach to micro-damage problems using deep learning for the prediction of loading deflection curves. The location of applied forces, dimensions of the specimen and material parameters are used as inputs of...
Deep learning technology has advanced rapidly and has started to be applied for the detection of welding defects. In the manufacturing process of power batteries for new energy vehicles, welding defects may occur due to the high directivity, converge...
The stochastic variance-reduced gradient (SVRG) theory is particularly well-suited for addressing gradient variance in deep neural network (DNN) training; however, its direct application to DNN training is hindered by adaptation challenges. To tackle...
Regularization techniques are critical in the development of machine learning models. Complex models, such as neural networks, are particularly prone to overfitting and to performing poorly on the training data.
We propose a novel dynamic gradient descent (DGD) framework integrated with reinforcement learning (RL) for AI-enhanced indoor environmental simulation, addressing the limitations of static optimization in dynamic settings. The proposed method combin...
The purpose of this study is to explore the factors that have the most decisive influence on actual learning intention that leads to participation in adult education. For developing the predictive model, we used tree-based machine learning, with the...
Training under extreme class imbalance (>1:100) remains an open problem in weakly supervised learning. The standard remedy—loss-level reweighting (focal loss, asymmetric loss, class-balanced loss)—is widely adopted, yet its behavior at...
Search-and-rescue (SAR) operations in disaster environments require drone swarms to coordinate efficiently despite incomplete information and potential communication failures. Existing stigmergy-based approaches provide low-bandwidth coordination but...
Aiming at a series of limitations of the Adam algorithm, such as hyperparameter sensitivity and unstable convergence, in this paper, an improved optimization algorithm, the Cycle-Norm-Adam (CN-Adam) algorithm, is proposed. The algorithm integrates th...
Alloy gradient-grained structures (represented by copper as a typical single-phase face-centered cubic (FCC) metal), known for their superior mechanical properties such as enhanced strength, ductility, and fatigue resistance, have become increasingly...
Rotating machinery is a core component of modern industry, and its operational state directly affects system safety and reliability. In order to achieve intelligent fault diagnosis of bearings under complex working conditions, the health management o...
The exchange of gradients is a widely used method in modelling systems for machine learning (e.g., distributed training, federated learning) in privacy-sensitive domains. Unfortunately, there are still privacy risks in federated learning, as servers...
There are several unsolved problems in federated learning, such as the security concerns and communication costs associated with it. Differential privacy (DP) offers effective privacy protection by introducing noise to parameters based on rigorous pr...
With the increasing demand for privacy preservation and strategy sharing in global financial markets, traditional centralized modeling approaches have become inadequate for multi-institutional collaborative tasks, particularly under the realistic cha...
This paper demonstrates a novel approach to training deep neural networks using a Mutual Information (MI)-driven, decaying Learning Rate (LR), Stochastic Gradient Descent (SGD) algorithm. MI between the output of the neural network and true outcomes...
Disability management in information systems refers to the process of ensuring that digital technologies and applications are designed to be accessible and usable by individuals with disabilities. Traditional methods face several challenges such as p...
The ability to rapidly produce accurate land use and land cover maps regularly and consistently has been a growing initiative as they have increasingly become an important tool in the efforts to evaluate, monitor, and conserve Earth’s natural r...
Recently, people’s demand for action recognition has extended from the initial high classification accuracy to the high accuracy of the temporal action detection. It is challenging to meet the two requirements simultaneously. The key to behavior reco...
Goldberg’s 100 Unipolar Markers remains one of the most popular ways to measure personality traits, in particular, the Big Five. An important reduction was later preformed by Saucier, using a sub-set of 40 markers. Both assessments are performe...
Accurate building construction cost prediction is critical, especially for sustainable projects (i.e., green buildings). Green building construction contracts are relatively new to the construction industry, where stakeholders have limited experience...
The FDA plans to gradually replace animal testing with organoid and organ-on-a-chip technologies for drug safety assessment, driving surging demand for gut-on-a-chip in food and drug safety evaluation and highlighting the need for efficient, precise...
Advances in software engineering, particularly in Agile software development (ASD), demand innovative approaches to effort estimation due to the volatility in Agile environments. Recent trends have made the automation of story point (SP) estimation i...
This paper proposes a novel mathematical theory of adaptation to convexity of loss functions based on the definition of the condense-discrete convexity (CDC) method. The developed theory is considered to be of immense value to stochastic settings and...
Optimizing heat treatment processes requires an understanding of the complex relationships between compositions, processing parameters, microstructures, and properties. Traditional experimental approaches are costly and time-consuming, whereas machin...
The prevalent utilization of deterministic strategy algorithms in Multi-Agent Deep Reinforcement Learning (MADRL) for collaborative tasks has posed a significant challenge in achieving stable and high-performance cooperative behavior. Addressing the...
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