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Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. [1] For example, for image classification, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks.
The IEEE Transactions on Learning Technologies (TLT) is a peer-reviewed scientific journal covering advances in the development of technologies for supporting human learning. It was established in 2008 and is published by the IEEE Education Society. [1] The current editor-in-chief (since 2022) is Minjuan Wang of San Diego State University.
The publications of the Institute of Electrical and Electronics Engineers (IEEE) constitute around 30% of the world literature in the electrical and electronics engineering and computer science fields, [citation needed] publishing well over 100 peer-reviewed journals. [1]
Synthetic data is generated to meet specific needs or certain conditions that may not be found in the original, real data. One of the hurdles in applying up-to-date machine learning approaches for complex scientific tasks is the scarcity of labeled data, a gap effectively bridged by the use of synthetic data, which closely replicates real experimental data. [3]
Along with ICLR and ICML, it is one of the three primary conferences of high impact in machine learning and artificial intelligence research. [ 1 ] The conference is currently a double-track meeting (single-track until 2015) that includes invited talks as well as oral and poster presentations of refereed papers, followed by parallel-track ...
IEEE Transactions on Neural Networks and Learning Systems is a monthly peer-reviewed scientific journal published by the IEEE Computational Intelligence Society. It covers the theory, design, and applications of neural networks and related learning systems. According to the Journal Citation Reports, the journal had a 2021 impact factor of 14. ...
Recurrent neural networks (RNNs) are a class of artificial neural networks designed for processing sequential data, such as text, speech, and time series, [1] where the order of elements is important.
Diagram of a Federated Learning protocol with smartphones training a global AI model. Federated learning (also known as collaborative learning) is a machine learning technique focusing on settings in which multiple entities (often referred to as clients) collaboratively train a model while ensuring that their data remains decentralized. [1]