Results 41 to 50 of about 8,398,730 (304)
Semi-supervised Sequence Learning
CoRR, 2015 We present two approaches that use unlabeled data to improve sequence learning with recurrent networks. The first approach is to predict what comes next in a sequence, which is a conventional language model in natural language processing. The second approach is to use a sequence autoencoder, which reads the input sequence into a vector and predicts the
Andrew M. Dai, Quoc V. Le
openaire +4 more sources
Semi-Supervised Deep Representation Learning [PDF]
, 2019 Deep neural networks need a lot of data to show their full potential in modeling and solving problems. However, in many real-world applications labeling data is expensive or not feasible while abundant unlabeled data is available.
Vahid Noroozi (8973485)
core +1 more source
Muffled Semi-Supervised Learning
CoRR, 2016 We explore a novel approach to semi-supervised learning. This approach is contrary to the common approach in that the unlabeled examples serve to "muffle," rather than enhance, the guidance provided by the labeled examples. We provide several variants of the basic algorithm and show experimentally that they can achieve significantly higher AUC than ...
Akshay Balsubramani, Yoav Freund
openaire +2 more sources
Semi-supervised Continual Learning with Meta Self-training
, 2022 Semi-supervised Continual Learning with Meta Self ...
Yunfeng Li (37710) +5 more
core +1 more source
Semi-supervised learning in cancer diagnostics
Frontiers in Oncology, 2022 In cancer diagnostics, a considerable amount of data is acquired during routine work-up. Recently, machine learning has been used to build classifiers that are tasked with cancer detection and aid in clinical decision-making.
Jan-Niklas Eckardt +8 more
doaj +1 more source
Towards Realistic Semi-supervised Learning
, 2022 Deep learning is pushing the state-of-the-art in many computer vision applications. However, it relies on large annotated data repositories, and capturing the unconstrained nature of the real-world data is yet to be solved. Semi-supervised learning (SSL) complements the annotated training data with a large corpus of unlabeled data to reduce annotation ...
Mamshad Nayeem Rizve +2 more
openaire +4 more sources
Semi-Supervised Learning strategy.
, 2016 Semi-Supervised Learning strategy.
Haifeng Li (142063) +6 more
core +1 more source
Semi-Supervised Relational Contrastive Learning [PDF]
, 2023 Disease diagnosis from medical images via supervised learning is usually dependent on tedious, error-prone, and costly image labeling by medical experts. Alternatively, semi-supervised learning and self-supervised learning offer effectiveness through the
Purpura-Pontoniere, Attiano +3 more
core
An Efficient Approach to Select Instances in Self-Training and Co-Training Semi-Supervised Methods
IEEE Access, 2022 Semi-supervised learning is a machine learning approach that integrates supervised and unsupervised learning mechanisms. In this learning, most of labels in the training set are unknown, while there is a small part of data that has known labels. The semi-
Karliane Medeiros Ovidio Vale +3 more
doaj +1 more source
South African Computer Journal, 2020 Semi-supervised learning is a potential solution for improving training data in low-resourced abusive language detection contexts such as South African abusive language detection on Twitter.
Oluwafemi Oriola, Eduan Kotzé
doaj +1 more source