Inductive transfer
View article: A survey of transfer learning
A survey of transfer learning Open
Machine learning and data mining techniques have been used in numerous real-world applications. An assumption of traditional machine learning methodologies is the training data and testing data are taken from the same domain, such that the…
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Continual lifelong learning with neural networks: A review Open
Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. This ability, referred to as lifelong learning, is mediated by a rich set of neurocognitive mechanisms that…
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A survey of machine learning for big data processing Open
There is no doubt that big data are now rapidly expanding in all science and engineering domains. While the potential of these massive data is undoubtedly significant, fully making sense of them requires new ways of thinking and novel lear…
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Transfer Learning in Natural Language Processing Open
The classic supervised machine learning paradigm is based on learning in isolation, a single predictive model for a task using a single dataset. This approach requires a large number of training examples and performs best for well-defined …
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Reminder of the First Paper on Transfer Learning in Neural Networks, 1976 Open
This paper describes a work on transfer learning in neural networks carried out in 1970s and early 1980s, which produced its first publication in 1976. In the contemporary research on transfer learning there is a belief that pioneering wor…
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Meta-Learning in Neural Networks: A Survey Open
The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent years. Contrary to conventional approaches to AI where tasks are solved from scratch using a fixed learning algorithm, meta-learning aims to i…
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A Comprehensive Survey on Transfer Learning Open
Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target domain data c…
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Learning from Few Examples: A Summary of Approaches to Few-Shot Learning Open
Few-Shot Learning refers to the problem of learning the underlying pattern in the data just from a few training samples. Requiring a large number of data samples, many deep learning solutions suffer from data hunger and extensively high co…
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Deep Learning (CNN) and Transfer Learning: A Review Open
Deep Learning is a machine learning area that has recently been used in a variety of industries. Unsupervised, semi-supervised, and supervised-learning are only a few of the strategies that have been developed to accommodate different type…
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Ensemble Transfer Learning Algorithm Open
Transfer learning and ensemble learning are the new trends for solving the problem that training data and test data have different distributions. In this paper, we design an ensemble transfer learning framework to improve the classificatio…
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An Integrated Transfer Learning and Multitask Learning Approach for Pharmacokinetic Parameter Prediction Open
The integrated transfer learning and multitask learning approach with the improved data set splitting algorithm was first introduced to predict the pharmacokinetic parameters. This method can be further employed in drug discovery and devel…
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Multitask Learning Over Graphs: An Approach for Distributed, Streaming Machine Learning Open
The problem of simultaneously learning several related tasks has received considerable attention in several domains, especially in machine learning, with the so-called multitask learning (MTL) problem, or learning to learn problem [1], [2]…
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A Systematic Literature Review on Transfer Learning for Predictive Maintenance in Industry 4.0 Open
The advent of Industry 4.0 has resulted in the widespread usage of novel paradigms and digital technologies within industrial production and manufacturing systems. The objective of making industrial operations monitoring easier also implie…
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Not Deep Learning but Autonomous Learning of Open Innovation for Sustainable Artificial Intelligence Open
What do we need for sustainable artificial intelligence that is not harmful but beneficial human life? This paper builds up the interaction model between direct and autonomous learning from the human’s cognitive learning process and firms’…
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Transfer Learning for Deep Learning on Graph-Structured Data Open
Graphs provide a powerful means for representing complex interactions between entities. Recently, new deep learning approaches have emerged for representing and modeling graph-structured data while the conventional deep learning methods, s…
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Transfer Learning: A New Promising Techniques Open
Transfer Learning is a machine learning technique that involves utilizing knowledge learned from one task to improve performance on another related task. This approach has been widely adopted in various fields such as computer vision, natu…
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Provable Meta-Learning of Linear Representations Open
Meta-learning, or learning-to-learn, seeks to design algorithms that can utilize previous experience to rapidly learn new skills or adapt to new environments. Representation learning -- a key tool for performing meta-learning -- learns a d…
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Completely Heterogeneous Transfer Learning with Attention - What And What Not To Transfer Open
We study a transfer learning framework where source and target datasets are heterogeneous in both feature and label spaces. Specifically, we do not assume explicit relations between source and target tasks a priori, and thus it is crucial …
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Virtual to Real-World Transfer Learning: A Systematic Review Open
Machine learning has become an important research area in many domains and real-world applications. The prevailing assumption in traditional machine learning techniques, that training and testing data should be of the same domain, is a cha…
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Taxonomy of machine learning paradigms: A data‐centric perspective Open
Machine learning is a field composed of various pillars. Traditionally, supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL) are the dominating learning paradigms that inspired the field since the 1950s. Ba…
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Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data Open
Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such…
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Multiround Transfer Learning and Modified Generative Adversarial Network for Lung Cancer Detection Open
Lung cancer has been the leading cause of cancer death for many decades. With the advent of artificial intelligence, various machine learning models have been proposed for lung cancer detection (LCD). Typically, challenges in building an a…
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Transfer Learning for Radio Frequency Machine Learning: A Taxonomy and Survey Open
Transfer learning is a pervasive technology in computer vision and natural language processing fields, yielding exponential performance improvements by leveraging prior knowledge gained from data with different distributions. However, whil…
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Knowledge-based Transfer Learning Explanation Open
Machine learning explanation can significantly boost machine learning's application in decision making, but the usability of current methods is limited in human-centric explanation, especially for transfer learning, an important machine le…
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Towards Image Classification with Machine Learning Methodologies for Smartphones Open
Recent developments in machine learning engendered many algorithms designed to solve diverse problems. More complicated tasks can be solved since numerous features included in much larger datasets are extracted by deep learning architectur…
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Improvement of Heterogeneous Transfer Learning Efficiency by Using Hebbian Learning Principle Open
Transfer learning algorithms have been widely studied for machine learning in recent times. In particular, in image recognition and classification tasks, transfer learning has shown significant benefits, and is getting plenty of attention …
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Learning What and Where to Transfer Open
As the application of deep learning has expanded to real-world problems with insufficient volume of training data, transfer learning recently has gained much attention as means of improving the performance in such small-data regime. Howeve…
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Multi-manufacturer drug identification based on near infrared spectroscopy and deep transfer learning Open
Near infrared (NIR) spectrum analysis technology has outstanding advantages such as rapid, nondestructive, pollution-free, and is widely used in food, pharmaceutical, petrochemical, agricultural products production and testing industries. …
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Transfer (machine) learning approaches coupled with target data augmentation to predict the mechanical properties of concrete Open
Transfer learning, a machine learning technique which employs prior knowledge from solving a source problem to solve a related target problem, is utilized in this work to predict the compressive strength and modulus of elasticity of differ…
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DL4ALL: Multi-Task Cross-Dataset Transfer Learning for Acute Lymphoblastic Leukemia Detection Open
Methods for the detection of Acute Lymphoblastic (or Lymphocytic) Leukemia (ALL) are increasingly considering Deep Learning (DL) due to its high accuracy in several fields, including medical imaging. In most cases, such methods use transfe…