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تعداد مقالات یافته شده: 3
ردیف عنوان نوع
1 Multi-level transfer learning for improving the performance of deep neural networks: Theory and practice from the tasks of facial emotion recognition and named entity recognition
یادگیری انتقال چند سطحی برای بهبود عملکرد شبکه های عصبی عمیق: نظریه و عمل از وظایف تشخیص احساسات چهره و شناسایی موجودیت-2021
Transfer learning has become a promising field in machine learning owing to its wide application prospects. Its effectiveness has spawned various methodologies and practices. Transfer learning refers to improving the performance of target learners in the target domain by transferring the knowledge contained in different yet related source domains. In other words, we can use data from additional domains or tasks to train a model with superior generalization. Using transfer learning, the dependence on considerable target-domain data can be reduced, thereby constructing target learners. Recently, the fields of computer vision (CV) and natural language processing (NLP) have witnessed the emergence of transfer learning, which has significantly improved the most advanced technology on a wide range of CV and NLP tasks. A typical approach of applying transfer learning to deep neural networks is to fine-tune a pretrained model of the source domain with data obtained from the target domain. This paper proposes a novel framework, based on the fine-tuning approach, called multilevel transfer learning (mLTL). Under this framework, we concluded the crucial findings and principles regarding the training sequence of related domain datasets and demonstrated its effectiveness by performing facial emotion and named entity recognition tasks. According to the experimental results, the deep neural network models using mLTL outperformed the original models on the target tasks.© 2021 Elsevier B.V. All rights reserved.
Keywords: Multilevel transfer learning | Computer vision | Natural language processing | Facial emotion recognition | Named entity recognition
مقاله انگلیسی
2 Machine learning in oil and gas; a SWOT analysis approach
یادگیری ماشین در نفت و گاز؛ یک روش تجزیه و تحلیل SWOT-2019
Digitalization of workflows using machine learning and advanced analytics is the new go-to strategy to add business value in the oil and gas industry. Enterprises strive to embrace these new technologies; but struggle to put their models in production, deliver tangible results and obtain favorable returns on investment. This paper reviews some of the recent developments and practices in this area and offers a SWOT analysis for strategic management and technology enablement. It is concluded that to reap the full benefits of ML in mission-critical tasks, oil and gas industry must leverage the latest technology developments, have a consistent strategic focus and build agile and collaborative teams of data scientists and domain experts.
Keywords: Machine learning | Analytics | SWOT | IoT | Transfer learning | Technology
مقاله انگلیسی
3 Online Transfer Learning
آموزش انتقال آنلاین-2014
In this paper, we propose a novel machine learning framework called “Online Transfer Learning” (OTL), which aims to attack an online learning task on a target domain by transferring knowledge from some source domain. We do not assume data in the target domain follows the same distribution as that in the source domain, and the motivation of our work is to enhance a supervised online learning task on a target domain by exploiting the existing knowledge that had been learnt from training data in source domains. OTL is in general very challenging since data in both source and target domains not only can be different in their class distributions, but also can be diverse in their feature representations. As a first attempt to this new research problem, we investigate two different settings of OTL: (i) OTL on homogeneous domains of common feature space, and (ii) OTL across heterogeneous domains of different feature spaces. For each setting, we propose effective OTL algorithms to solve online classification tasks, and show some theoretical bounds of the algorithms. In addition, we also apply the OTL technique to attack the challenging online learning tasks with concept-drifting data streams. Finally, we conduct extensive empirical studies on a comprehensive testbed, in which encouraging results validate the efficacy of our techniques. Keywords: Transfer learning Online learning Knowledge transfer
مقاله انگلیسی
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