Results 31 to 40 of about 5,885,991 (322)
npj Digit. Medicine, 2023 Advancements in deep learning and computer vision provide promising solutions for medical image analysis, potentially improving healthcare and patient outcomes.
Shih-Cheng Huang +5 more
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Supervised Learning of Universal Sentence Representations from Natural Language Inference Data [PDF]
Conference on Empirical Methods in Natural Language Processing, 2017 Many modern NLP systems rely on word embeddings, previously trained in an unsupervised manner on large corpora, as base features. Efforts to obtain embeddings for larger chunks of text, such as sentences, have however not been so successful.
Alexis Conneau +4 more
semanticscholar +1 more source
Longitudinal self-supervised learning [PDF]
Medical Image Analysis, 2021 Machine learning analysis of longitudinal neuroimaging data is typically based on supervised learning, which requires a large number of ground-truth labels to be informative. As ground-truth labels are often missing or expensive to obtain in neuroscience, we avoid them in our analysis by combing factor disentanglement with self-supervised learning to ...
Zixuan Liu +4 more
openaire +3 more sources
A reawakening of Machine Learning Application in Unmanned Aerial Vehicle: Future Research Motivation
EAI Endorsed Transactions on Internet of Things, 2022 Machine learning (ML) entails artificial procedures that improve robotically through experience and using data. Supervised, unsupervised, semi-supervised, and Reinforcement Learning (RL) are the main types of ML. This study mainly focuses on RL and Deep
Wasswa Shafik +3 more
doaj +1 more source
Sensors, 2022 The total boll count from a plant is one of the most important phenotypic traits for cotton breeding and is also an important factor for growers to estimate the final yield.
Shrinidhi Adke +3 more
doaj +1 more source
Self-supervised Learning: A Succinct Review
Archives of Computational Methods in Engineering, 2023 Machine learning has made significant advances in the field of image processing. The foundation of this success is supervised learning, which necessitates annotated labels generated by humans and hence learns from labelled data, whereas unsupervised ...
V. Rani +4 more
semanticscholar +1 more source
Semi-supervised Learning Algorithm Based on Maximum Margin and Manifold Hypothesis [PDF]
Jisuanji kexueSemi-supervised learning is a weakly supervised learning pattern between supervised learning and unsupervised lear-ning.It combines a small number of labeled instances with a large number of unlabeled instances to build a model during the process of ...
DAI Wei, CHAI Jing, LIU Yajiao
doaj +1 more source
Physics-constrained indirect supervised learning
Theoretical and Applied Mechanics Letters, 2020 : This study proposes a supervised learning method that does not rely on labels. We use variables associated with the label as indirect labels, and construct an indirect physics-constrained loss based on the physical mechanism to train the model.
Yuntian Chen, Dongxiao Zhang
doaj +1 more source
An Improved Algorithm of Drift Compensation for Olfactory Sensors
Applied Sciences, 2022 This research mainly studies the semi-supervised learning algorithm of different domain data in machine olfaction, also known as sensor drift compensation algorithm.
Siyu Lu +6 more
doaj +1 more source
S3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization [PDF]
International Conference on Information and Knowledge Management, 2020 Recently, significant progress has been made in sequential recommendation with deep learning. Existing neural sequential recommendation models usually rely on the item prediction loss to learn model parameters or data representations.
Kun Zhou +7 more
semanticscholar +1 more source