nimpressive surveys on transfer learning, these surveys introduce approaches in\na relatively isolated way and lack the recent advances in transfer learning.\nDue to the rapid expansion of the transfer learning area, it is both necessary\nand challenging to comprehensively review the relevant studies....
Some studies conducted a comprehensive review focused primarily on DL in the medical domain. Litjens et al. [16] reviewed DL for medical image analysis by summarizing over 300 articles, while Chowdhury et al. [17] reviewed the state-of-the-art research on self-supervised learning in medicine....
The present work provides a comprehensive overview of transfer learning applications in smart buildings, classifying and analyzing 77 papers according to their applications, algorithms, and adopted metrics. The study identified four main application areas of transfer learning: (1) building load prediction...
To mitigate data limitations, transfer learning (TL) offers a viable solution by allowing pre-trained models to be adapted for agricultural applications. Many researchers have demonstrated TL’s potential to advance agriculture. Despite its importance, there is a lack of a comprehensive review, which...
“black-box” machines that hamper the standard development of deep learning research and applications. Thus for clear understanding, in this paper, we present a structured and comprehensive view on DL techniques considering the variations in real-world problems and tasks. To achieve our goal, we ...
1 A Survey on Transfer Learning Sinno Jialin Pan and Qiang Yang Fellow, IEEE Abstract—A major assumption in many machine learning and data mining algorithms is that the training and future data must be in the same feature space and have the same distribution. However, in many real-world app...
An Empirical Survey of Unsupervised Text Representation Methods on Twitter Data. W-NUT@EMNLP 2020 paper bib Lili Wang, Chongyang Gao, Jason Wei, Weicheng Ma, Ruibo Liu, Soroush Vosoughi Bangla Natural Language Processing: A Comprehensive Review of Classical, Machine Learning, and Deep Learning Bas...
⭐ Kashgari Transfer Learning with focus on Chinese [GitHub, 2389 stars] ⭐ Underthesea - Vietnamese NLP Toolkit [GitHub, 1383 stars] ⭐ PTT5 - Pretraining and validating the T5 model on Brazilian Portuguese data [GitHub, 84 stars] Text Data Labelling & Classification ⭐ Small-Text ...
This comprehensive review investigates the multifaceted applications of machine learning in materials research across six key dimensions, redefining the field's boundaries. It explains various knowledge acquisition mechanisms starting with supervised, unsupervised, reinforcement, and deep learning techniques. Thes...
Existing traffic surveys focus on classification and do not consider anonymization. Here, we review the Internet traffic classification and obfuscation techniques, largely considering the ML-based solutions. In addition, this paper presents a comprehensive review of various data representation methods, and...