Results 41 to 50 of about 137,057 (303)
A Single-Stage Unsupervised Denoising Low-Illumination Enhancement Network Based on Swin-Transformer
IEEE Access, 2023 Traditional low-light enhancement methods are often based on paired datasets for training. The training data is difficult to obtain and the resulting model has poor generalization.
Qian Zhang +3 more
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Self-Supervised and Few-Shot Contrastive Learning Frameworks for Text Clustering
IEEE Access, 2023 Contrastive learning is a promising approach to unsupervised learning, as it inherits the advantages of well-studied deep models without a dedicated and complex model design. In this paper, based on bidirectional encoder representations from transformers
Haoxiang Shi, Tetsuya Sakai
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Unsupervised Learning of Temporal Abstractions with Slot-Based Transformers [PDF]
, 2022 The discovery of reusable subroutines simplifies decision making and planning in complex reinforcement learning problems. Previous approaches propose to learn such temporal abstractions in an unsupervised fashion through observing state-action ...
van Steenkiste, Sjoerd +3 more
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Nuclear Engineering and Technology, 2022 This paper proposes an algorithm for signal validation using unsupervised methods in emergency situations at nuclear power plants (NPPs) when signals are rapidly changing.
Younhee Choi +2 more
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Unsupervised learning in noise [PDF]
IEEE Transactions on Neural Networks, 1990 A new hybrid learning law, the differential competitive law, which uses the neuronal signal velocity as a local unsupervised reinforcement mechanism, is introduced, and its coding and stability behavior in feedforward and feedback networks is examined.
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On The Philosophy of Unsupervised Learning
SSRN Electronic Journal, 2022 AbstractUnsupervised learning algorithms are widely used for many important statistical tasks with numerous applications in science and industry. Yet despite their prevalence, they have attracted remarkably little philosophical scrutiny to date. This stands in stark contrast to supervised and reinforcement learning algorithms, which have been widely ...
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Towards Open Ended Learning: Budgets, Model Selection, and Representation [PDF]
, 2011 Biological organisms learn to recognize visual categories continuously over the course of their lifetimes. This impressive capability allows them to adapt to new circumstances as they arise, and to flexibly incorporate new object categories as they are ...
Gomes, Ryan Geoffrey
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SUN: Stochastic UNsupervised Learning for Data Noise and Uncertainty Reduction
Applied SciencesUnsupervised learning methods significantly benefit various practical applications by effectively identifying intrinsic patterns within unlabelled data.
Nicholas Christakis, Dimitris Drikakis
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A comment on the training of unsupervised neural networks for learning phases
Results in Physics, 2022 The impact on the performance of an unsupervised neural network (NN) for learning the phases of two-dimensional ferromagnetic Potts model, namely a deep learning autoencoder (AE), from using various training sets is investigated.
Yuan-Heng Tseng, Fu-Jiun Jiang
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Unsupervised Learning of Visual Structure [PDF]
Journal of Vision, 2002 To learn a visual code in an unsupervised manner, one may attempt to capture those features of the stimulus set that would contribute significantly to a statistically efficient representation (as dictated, e.g., by the Minimum Description Length principle).
Shimon Edelman +2 more
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