Results 61 to 70 of about 10,380,352 (315)
Constrained Deep Transfer Feature Learning and its Applications
, 2017 Feature learning with deep models has achieved impressive results for both data representation and classification for various vision tasks. Deep feature learning, however, typically requires a large amount of training data, which may not be feasible for ...
Ji, Qiang, Wu, Yue
core +1 more source
Learning More Universal Representations for Transfer-Learning [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 Submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Tamaazousti, Youssef +4 more
openaire +4 more sources
Ensemble Transfer Learning Algorithm
IEEE Access, 2018 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 classification accuracy when
Xiaobo Liu +4 more
doaj +1 more source
Science communication on TikTok: toward transformative and post-normal science
Frontiers in CommunicationScience communication on social media is becoming increasingly important in order to promote an open dialog between science and the public. This raises the question of how to present topics related to climate change in a way that is both scientific and ...
Claudia Frick +2 more
doaj +1 more source
Evaluating Protein Transfer Learning with TAPE [PDF]
bioRxiv, 2019 Protein modeling is an increasingly popular area of machine learning research. Semi-supervised learning has emerged as an important paradigm in protein modeling due to the high cost of acquiring supervised protein labels, but the current literature is ...
Roshan Rao +7 more
semanticscholar +1 more source
Smart City Development with Urban Transfer Learning
, 2018 Nowadays, the smart city development levels of different cities are still unbalanced. For a large number of cities which just started development, the governments will face a critical cold-start problem: 'how to develop a new smart city service with ...
Guo, Bin, Wang, Leye, Yang, Qiang
core +2 more sources
FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare [PDF]
IEEE Intelligent Systems, 2019 With the rapid development of computing technology, wearable devices make it easy to get access to people's health information. Smart healthcare achieves great success by training machine learning models on a large quantity of user personal data. However,
Yiqiang Chen +4 more
semanticscholar +1 more source
Deep Learning (CNN) and Transfer Learning: A Review
Journal of Physics: Conference Series, 2022 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 types of learning ...
Jaya Gupta, Sunil Pathak, G. Kumar
semanticscholar +1 more source
Transfer Learning for Radio Frequency Machine Learning: A Taxonomy and Survey
Sensors, 2022 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.
Lauren J. Wong, Alan J. Michaels
doaj +1 more source
“Transfer Talk” in Talk about Writing in Progress: Two Propositions about Transfer of Learning [PDF]
, 2019 This article tracks the emergence of the concept of “transfer talk”—a concept distinct from transfer of learning—and teases out the implications of transfer talk for theories of transfer of learning.
Bodee, Bridget +6 more
core +1 more source