Results 51 to 60 of about 10,380,352 (315)
Meta-Transfer Learning for Few-Shot Learning [PDF]
Computer Vision and Pattern Recognition, 2018 Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few ...
Qianru Sun +3 more
semanticscholar +1 more source
A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification
Journal of Healthcare Engineering, 2022 Text Classification problem has been thoroughly studied in information retrieval problems and data mining tasks. It is beneficial in multiple tasks including medical diagnose health and care department, targeted marketing, entertainment industry, and ...
Rukhma Qasim +3 more
semanticscholar +1 more source
Lautum Regularization for Semi-supervised Transfer Learning [PDF]
, 2020 Transfer learning is a very important tool in deep learning as it allows propagating information from one "source dataset" to another "target dataset", especially in the case of a small number of training examples in the latter.
Giryes, Raja +2 more
core +1 more source
Effects of Force Level and Hand Dominance on Bilateral Transfer of a Fine Motor Skill [PDF]
, 2016 Our research is about bilateral transfer, a concept in motor learning where skills learned by one limb are "transferred", allowing the opposite limb to benefit from what was learned by the first limb.
Muller, Karl, Rai, Aakarsh
core +1 more source
Transfer Learning in Deep Reinforcement Learning: A Survey [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recent years have witnessed remarkable progress in reinforcement learning upon the fast development of deep neural networks. Along with the promising prospects
Zhuangdi Zhu +3 more
semanticscholar +1 more source
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019 In machine learning, transfer learning is concerned with utilising prior knowledge as a way to improve the process of training a new model in a different, but related, domain. Transfer learning has been shown to be beneficial across a large set of problems.
Brandon Muller +3 more
openaire +2 more sources
Stochastic Ensemble Policy Transfer [PDF]
Jisuanji kexue yu tansuo, 2022 Reinforcement learning (RL) has achieved great success on sequential decision-making problems. Along with the fast advances of RL, transfer learning (TL) arises as an important technique to accelerate the learning process of RL by leveraging and ...
CHANG Tian, ZHANG Zongzhang, YU Yang
doaj +1 more source
Transfer Learning for Speech and Language Processing [PDF]
, 2015 Transfer learning is a vital technique that generalizes models trained for one setting or task to other settings or tasks. For example in speech recognition, an acoustic model trained for one language can be used to recognize speech in another language ...
Wang, Dong, Zheng, Thomas Fang
core +1 more source
Exploring Metacognition as Support for Learning Transfer
Teaching & Learning Inquiry: The ISSOTL Journal, 2017 The ability to transfer learning to new situations lies at the heart of lifelong learning and the employability of university graduates. Because students are often unaware of the importance of learning transfer and staff do not always explicitly ...
Lauren Scharff +6 more
doaj +1 more source
Climate ServicesCommunities in drought-prone areas continued to fall into new vulnerability traps due to increasing water demand and stress. The study assessed groundwater development and management constraints in the Chiredzi and Zvishavane districts of Zimbabwe ...
Pascal Manyakaidze +2 more
doaj +1 more source