Due to the dominant position of deep learning (mostly deep neural networks) in various artificial intelligence applications, recently, ensemble learning ba
In this paper, we propose a robust Selective Review Learning (NSRL) framework for NER task with noisy labels. Specifically, we design a Status Loss Function (SLF) which helps the model review the previous knowledge continuously when learning new knowledge, and prevents model from overfitting noisy...
Machine learning Deep learning 1. Introduction Agriculture, a focus of scientific and technological innovation, has seen significant transformations due to technology (Montero et al., 2020). Crop yield estimation is a critical aspect, depending on various factors, including agrometeorological variables li...
To this end, we first show a big picture of most common learning models used in the studied papers (see Section 2). Then, we present an overview of the arrhythmias from the medical perspective (see Section 3), performance evaluation metrics of ECG classifiers (see Section 5), and the ...
This survey aims to provide a comprehensive overview of the application of LLM in cybersecurity. We seek to address three key questions: RQ1: How to construct cybersecurity-oriented domain LLMs? RQ2: What are the potential applications of LLMs in cybersecurity? RQ3: What are the challenge ...
the sources of label noise, theimpact of label noise, the detection of label noise, label noise handling techniques, and theirevaluation. Categorization of both label noise detection methods and handling techniques areprovided.Discussion: From a methodological perspective, we observe that the medical ...
a recent survey on informed machine learning [17] and will provide a roadmap from the knowledge sources, knowledge representations and the knowledge embedding approaches. Especially for PHM task, this roadmap will emphasize the currently mainstream trends of KDML in terms of knowledge source and ...
As one of the latest advances in AI and ML, deep learning (DL), which transforms the data through layers of nonlinear computational processing units, provides a new paradigm to effectively gain knowledge from complex data7. In recent years, DL algorithms have demonstrated superior performance in ...
of the teacher. This paper provides a comprehensive survey of the literature on IRL. This survey outlines the differences between IRL and two similar methods - apprenticeship learning and inverse optimal control. Further, this survey organizes the IRL literature based on the principal method, ...
Machine Learning (ML) has revolutionized various fields, enabling the development of intelligent systems capable of solving complex problems. However, the