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Gotowe bibliografie tematyczne / Unsupervised neural networks / Artykuły w czasopismach

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Artykuły w czasopismach na temat „Unsupervised neural networks”

Autor: Grafiati

Data publikacji: 4 czerwca 2021

Data aktualizacji: 19 lutego 2022

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1

Murnion, Shane D. "Spatial analysis using unsupervised neural networks." Computers & Geosciences 22, no. 9 (November 1996): 1027–31. http://dx.doi.org/10.1016/s0098-3004(96)00041-6.

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Luo, Shuyue, Shangbo Zhou, Yong Feng, and Jiangan Xie. "Pansharpening via Unsupervised Convolutional Neural Networks." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 (2020): 4295–310. http://dx.doi.org/10.1109/jstars.2020.3008047.

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Meuleman, J., and C. van Kaam. "UNSUPERVISED IMAGE SEGMENTATION WITH NEURAL NETWORKS." Acta Horticulturae, no. 562 (November 2001): 101–8. http://dx.doi.org/10.17660/actahortic.2001.562.10.

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4

Gunhan, Atilla E., László P. Csernai, and Jørgen Randrup. "UNSUPERVISED COMPETITIVE LEARNING IN NEURAL NETWORKS." International Journal of Neural Systems 01, no. 02 (January 1989): 177–86. http://dx.doi.org/10.1142/s0129065789000086.

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We study an idealized neural network that may approximate certain neurophysiological features of natural neural systems. The network contains a mutual lateral inhibition and is subjected to unsupervised learning by means of a Hebb-type learning principle. Its learning ability is analysed as a function of the strength of lateral inhibition and the training set.

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5

Becker, Suzanna. "UNSUPERVISED LEARNING PROCEDURES FOR NEURAL NETWORKS." International Journal of Neural Systems 02, no. 01n02 (January 1991): 17–33. http://dx.doi.org/10.1142/s0129065791000030.

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Supervised learning procedures for neural networks have recently met with considerable success in learning difficult mappings. However, their range of applicability is limited by their poor scaling behavior, lack of biological plausibility, and restriction to problems for which an external teacher is available. A promising alternative is to develop unsupervised learning algorithms which can adaptively learn to encode the statistical regularities of the input patterns, without being told explicitly the correct response for each pattern. In this paper, we describe the major approaches that have

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6

Hamad, D., C. Firmin, and J. G. Postaire. "Unsupervised pattern classification by neural networks." Mathematics and Computers in Simulation 41, no. 1-2 (June 1996): 109–16. http://dx.doi.org/10.1016/0378-4754(95)00063-1.

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Vamaraju, Janaki, and Mrinal K. Sen. "Unsupervised physics-based neural networks for seismic migration." Interpretation 7, no. 3 (August 1, 2019): SE189—SE200. http://dx.doi.org/10.1190/int-2018-0230.1.

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We have developed a novel framework for combining physics-based forward models and neural networks to advance seismic processing and inversion algorithms. Migration is an effective tool in seismic data processing and imaging. Over the years, the scope of these algorithms has broadened; today, migration is a central step in the seismic data processing workflow. However, no single migration technique is suitable for all kinds of data and all styles of acquisition. There is always a compromise on the accuracy, cost, and flexibility of these algorithms. On the other hand, machine-learning algorith

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8

Xu, Jianqiao, Zhaolu Zuo, Danchao Wu, Bing Li, Xiaoni Li, and Deyi Kong. "Bearing Defect Detection with Unsupervised Neural Networks." Shock and Vibration 2021 (August 19, 2021): 1–11. http://dx.doi.org/10.1155/2021/9544809.

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Bearings always suffer from surface defects, such as scratches, black spots, and pits. Those surface defects have great effects on the quality and service life of bearings. Therefore, the defect detection of the bearing has always been the focus of the bearing quality control. Deep learning has been successfully applied to the objection detection due to its excellent performance. However, it is difficult to realize automatic detection of bearing surface defects based on data-driven-based deep learning due to few samples data of bearing defects on the actual production line. Sample preprocessin

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9

Raja, Muhammad Asif Zahoor. "Unsupervised neural networks for solving Troesch's problem." Chinese Physics B 23, no. 1 (January 2014): 018903. http://dx.doi.org/10.1088/1674-1056/23/1/018903.

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10

Parisi, Daniel R., Marı́a C. Mariani, and Miguel A. Laborde. "Solving differential equations with unsupervised neural networks." Chemical Engineering and Processing: Process Intensification 42, no. 8-9 (August 2003): 715–21. http://dx.doi.org/10.1016/s0255-2701(02)00207-6.

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11

Ergen, Tolga, and Suleyman Serdar Kozat. "Unsupervised Anomaly Detection With LSTM Neural Networks." IEEE Transactions on Neural Networks and Learning Systems 31, no. 8 (August 2020): 3127–41. http://dx.doi.org/10.1109/tnnls.2019.2935975.

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12

Jonker, H. J. J., and A. C. C. Coolen. "Unsupervised dynamic learning in layered neural networks." Journal of Physics A: Mathematical and General 24, no. 17 (September 7, 1991): 4219–34. http://dx.doi.org/10.1088/0305-4470/24/17/032.

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13

Zhou, Errui, Liang Fang, and Binbin Yang. "Memristive Spiking Neural Networks Trained with Unsupervised STDP." Electronics 7, no. 12 (December 6, 2018): 396. http://dx.doi.org/10.3390/electronics7120396.

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Neuromorphic computing systems are promising alternatives in the fields of pattern recognition, image processing, etc. especially when conventional von Neumann architectures face several bottlenecks. Memristors play vital roles in neuromorphic computing systems and are usually used as synaptic devices. Memristive spiking neural networks (MSNNs) are considered to be more efficient and biologically plausible than other systems due to their spike-based working mechanism. In contrast to previous SNNs with complex architectures, we propose a hardware-friendly architecture and an unsupervised spike-

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14

Resta, Marina, Michele Sonnessa, Elena Tànfani, and Angela Testi. "Unsupervised neural networks for clustering emergent patient flows." Operations Research for Health Care 18 (September 2018): 41–51. http://dx.doi.org/10.1016/j.orhc.2017.08.002.

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15

Ryu, Jongbin, Ming-Hsuan Yang, and Jongwoo Lim. "Unsupervised feature learning for self-tuning neural networks." Neural Networks 133 (January 2021): 103–11. http://dx.doi.org/10.1016/j.neunet.2020.10.011.

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16

Hinton, G., P. Dayan, B. Frey, and R. Neal. "The "wake-sleep" algorithm for unsupervised neural networks." Science 268, no. 5214 (May 26, 1995): 1158–61. http://dx.doi.org/10.1126/science.7761831.

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17

Begin, J., and R. Proulx. "Categorization in unsupervised neural networks: the Eidos model." IEEE Transactions on Neural Networks 7, no. 1 (January 1996): 147–54. http://dx.doi.org/10.1109/72.478399.

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18

Elazouni, Ashraf M. "Classifying Construction Contractors Using Unsupervised-Learning Neural Networks." Journal of Construction Engineering and Management 132, no. 12 (December 2006): 1242–53. http://dx.doi.org/10.1061/(asce)0733-9364(2006)132:12(1242).

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19

MUHAMMED, HAMED HAMID. "UNSUPERVISED FUZZY CLUSTERING USING WEIGHTED INCREMENTAL NEURAL NETWORKS." International Journal of Neural Systems 14, no. 06 (December 2004): 355–71. http://dx.doi.org/10.1142/s0129065704002121.

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A new more efficient variant of a recently developed algorithm for unsupervised fuzzy clustering is introduced. A Weighted Incremental Neural Network (WINN) is introduced and used for this purpose. The new approach is called FC-WINN (Fuzzy Clustering using WINN). The WINN algorithm produces a net of nodes connected by edges, which reflects and preserves the topology of the input data set. Additional weights, which are proportional to the local densities in input space, are associated with the resulting nodes and edges to store useful information about the topological relations in the given inp

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20

Atiya, Amir F. "An unsupervised learning technique for artificial neural networks." Neural Networks 3, no. 6 (January 1990): 707–11. http://dx.doi.org/10.1016/0893-6080(90)90058-s.

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21

Pavlidis, N. G., V. P. Plagianakos, D. K. Tasoulis, and M. N. Vrahatis. "Financial forecasting through unsupervised clustering and neural networks." Operational Research 6, no. 2 (May 2006): 103–27. http://dx.doi.org/10.1007/bf02941227.

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22

Bernert, Marie, and Blaise Yvert. "An Attention-Based Spiking Neural Network for Unsupervised Spike-Sorting." International Journal of Neural Systems 29, no. 08 (September 25, 2019): 1850059. http://dx.doi.org/10.1142/s0129065718500594.

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Bio-inspired computing using artificial spiking neural networks promises performances outperforming currently available computational approaches. Yet, the number of applications of such networks remains limited due to the absence of generic training procedures for complex pattern recognition, which require the design of dedicated architectures for each situation. We developed a spike-timing-dependent plasticity (STDP) spiking neural network (SSN) to address spike-sorting, a central pattern recognition problem in neuroscience. This network is designed to process an extracellular neural signal i

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23

Krotov, Dmitry, and John J. Hopfield. "Unsupervised learning by competing hidden units." Proceedings of the National Academy of Sciences 116, no. 16 (March 29, 2019): 7723–31. http://dx.doi.org/10.1073/pnas.1820458116.

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It is widely believed that end-to-end training with the backpropagation algorithm is essential for learning good feature detectors in early layers of artificial neural networks, so that these detectors are useful for the task performed by the higher layers of that neural network. At the same time, the traditional form of backpropagation is biologically implausible. In the present paper we propose an unusual learning rule, which has a degree of biological plausibility and which is motivated by Hebb’s idea that change of the synapse strength should be local—i.e., should depend only on the activi

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24

Aragon-Calvo, M. A., and J. C. Carvajal. "Self-supervised learning with physics-aware neural networks – I. Galaxy model fitting." Monthly Notices of the Royal Astronomical Society 498, no. 3 (September 7, 2020): 3713–19. http://dx.doi.org/10.1093/mnras/staa2228.

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ABSTRACT Estimating the parameters of a model describing a set of observations using a neural network is, in general, solved in a supervised way. In cases when we do not have access to the model’s true parameters, this approach can not be applied. Standard unsupervised learning techniques, on the other hand, do not produce meaningful or semantic representations that can be associated with the model’s parameters. Here we introduce a novel self-supervised hybrid network architecture that combines traditional neural network elements with analytic or numerical models, which represent a physical pr

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25

Zhang, Pengfei, and Xiaoming Ju. "Adversarial Sample Detection with Gaussian Mixture Conditional Generative Adversarial Networks." Mathematical Problems in Engineering 2021 (September 13, 2021): 1–18. http://dx.doi.org/10.1155/2021/8268249.

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It is important to detect adversarial samples in the physical world that are far away from the training data distribution. Some adversarial samples can make a machine learning model generate a highly overconfident distribution in the testing stage. Thus, we proposed a mechanism for detecting adversarial samples based on semisupervised generative adversarial networks (GANs) with an encoder-decoder structure; this mechanism can be applied to any pretrained neural network without changing the network’s structure. The semisupervised GANs also give us insight into the behavior of adversarial sample

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26

Lo, James Ting-Ho. "A Low-Order Model of Biological Neural Networks." Neural Computation 23, no. 10 (October 2011): 2626–82. http://dx.doi.org/10.1162/neco_a_00166.

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A biologically plausible low-order model (LOM) of biological neural networks is proposed. LOM is a recurrent hierarchical network of models of dendritic nodes and trees; spiking and nonspiking neurons; unsupervised, supervised covariance and accumulative learning mechanisms; feedback connections; and a scheme for maximal generalization. These component models are motivated and necessitated by making LOM learn and retrieve easily without differentiation, optimization, or iteration, and cluster, detect, and recognize multiple and hierarchical corrupted, distorted, and occluded temporal and spati

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27

Chakravarty, Aniv, and Jagadish S. Kallimani. "Unsupervised Multi-Document Abstractive Summarization Using Recursive Neural Network with Attention Mechanism." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 3867–72. http://dx.doi.org/10.1166/jctn.2020.8976.

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Text summarization is an active field of research with a goal to provide short and meaningful gists from large amount of text documents. Extractive text summarization methods have been extensively studied where text is extracted from the documents to build summaries. There are various type of multi document ranging from different formats to domains and topics. With the recent advancement in technology and use of neural networks for text generation, interest for research in abstractive text summarization has increased significantly. The use of graph based methods which handle semantic informati

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28

Browne, David, Michael Giering, and Steven Prestwich. "PulseNetOne: Fast Unsupervised Pruning of Convolutional Neural Networks for Remote Sensing." Remote Sensing 12, no. 7 (March 29, 2020): 1092. http://dx.doi.org/10.3390/rs12071092.

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Scene classification is an important aspect of image/video understanding and segmentation. However, remote-sensing scene classification is a challenging image recognition task, partly due to the limited training data, which causes deep-learning Convolutional Neural Networks (CNNs) to overfit. Another difficulty is that images often have very different scales and orientation (viewing angle). Yet another is that the resulting networks may be very large, again making them prone to overfitting and unsuitable for deployment on memory- and energy-limited devices. We propose an efficient deep-learnin

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29

Mihaylov, Oleg, Galina Nikolcheva, and Peter Popov. "SETUP GENERATION USING NEURAL NETWORKS." CBU International Conference Proceedings 5 (September 24, 2017): 1169–74. http://dx.doi.org/10.12955/cbup.v5.1090.

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The article presents an unsupervised learning algorithm that groups technological features in a setup for machining process. Setup generation is one of the most important tasks in automated process planning and in fixture configuration. A setup is created based on approach direction of the features. The algorithm proposed in this work generates a neural network that determines the setup each feature belongs to, and the number of setups generated is minimal. This algorithm, unlike others, is not influenced by the order of the input sequence. Parallel implementation of the algorithm is straightf

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30

Gu, Ming. "The Algorithm of Quadratic Junction Neural Network." Applied Mechanics and Materials 462-463 (November 2013): 438–42. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.438.

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Neural network with quadratic junction was described. Structure, properties and unsupervised learning rules of the neural network were discussed. An ART-based hierarchical clustering algorithm using this kind of neural networks was suggested. The algorithm can determine the number of clusters and clustering data. A 2-D artificial data set is used to illustrate and compare the effectiveness of the proposed algorithm and K-means algorithm.

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31

Zhuang, Chengxu, Siming Yan, Aran Nayebi, Martin Schrimpf, Michael C. Frank, James J. DiCarlo, and Daniel L. K. Yamins. "Unsupervised neural network models of the ventral visual stream." Proceedings of the National Academy of Sciences 118, no. 3 (January 11, 2021): e2014196118. http://dx.doi.org/10.1073/pnas.2014196118.

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Deep neural networks currently provide the best quantitative models of the response patterns of neurons throughout the primate ventral visual stream. However, such networks have remained implausible as a model of the development of the ventral stream, in part because they are trained with supervised methods requiring many more labels than are accessible to infants during development. Here, we report that recent rapid progress in unsupervised learning has largely closed this gap. We find that neural network models learned with deep unsupervised contrastive embedding methods achieve neural predi

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32

Supatman, Supatman, and Sri Ayem. "UMKM Clusterization with Unsupervised Neural Networks Method for Accounting by Business Capital." TAMANSISWA INTERNATIONAL JOURNAL IN EDUCATION AND SCIENCE 2, no. 1 (October 27, 2020): 33–39. http://dx.doi.org/10.30738/tijes.v2i1.7698.

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UMKM menurut pasal (6) UU nomor 20 tahun 2008 berdasarkan asset dan omset dibagi menjadi tiga kriteria yaitu UMi (Usaha Mikro), UK (Usaha Kecil) dan UM (Usaha Menengah). Sementara itu variabel dalam laporan BPS terkait UMKM meliputi Unit Usaha, Tenaga Kerja, PDB atas usaha yang berlaku, PDB atas dasar harga konstan 2000, Total Ekspor Non Migas, Investasi atas dasar harga berlaku, Investasi atas dasar harga konstan 2000. Sehingga pendekatan untuk melakukan kriteria berdasarkan asset dan omset relatif lemah mengingat secara rinci terdapat 7 variabel pendukung kriteria (berdasarkan data BPS).Unsu

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33

Sharma, Rachita, and Sanjay Kumar Dubey. "ANALYSIS OF SOM & SOFM TECHNIQUES USED IN SATELLITE IMAGERY." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 2 (June 21, 2018): 563–65. http://dx.doi.org/10.24297/ijct.v4i2c1.4181.

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This paper describes the introduction of Supervised and Unsupervised Techniques with the comparison of SOFM (Self Organized Feature Map) used for Satellite Imagery. In this we have explained the way of spatial and temporal changes detection used in forecasting in satellite imagery. Forecasting is based on time series of images using Artificial Neural Network. Recently neural networks have gained a lot of interest in time series prediction due to their ability to learn effectively nonlinear dependencies from large volume of possibly noisy data with a learning algorithm. Unsupervised neural netw

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PAVLIDIS, N. G., D. K. TASOULIS, V. P. PLAGIANAKOS, and M. N. VRAHATIS. "COMPUTATIONAL INTELLIGENCE METHODS FOR FINANCIAL TIME SERIES MODELING." International Journal of Bifurcation and Chaos 16, no. 07 (July 2006): 2053–62. http://dx.doi.org/10.1142/s0218127406015891.

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In this paper, the combination of unsupervised clustering algorithms with feedforward neural networks in exchange rate time series forecasting is studied. Unsupervised clustering algorithms have the desirable property of deciding on the number of partitions required to accurately segment the input space during the clustering process, thus relieving the user from making this ad hoc choice. Combining this input space partitioning methodology with feedforward neural networks acting as local predictors for each identified cluster helps alleviate the problem of nonstationarity frequently encountere

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35

Crawford, Eric, and Joelle Pineau. "Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3412–20. http://dx.doi.org/10.1609/aaai.v33i01.33013412.

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There are many reasons to expect an ability to reason in terms of objects to be a crucial skill for any generally intelligent agent. Indeed, recent machine learning literature is replete with examples of the benefits of object-like representations: generalization, transfer to new tasks, and interpretability, among others. However, in order to reason in terms of objects, agents need a way of discovering and detecting objects in the visual world - a task which we call unsupervised object detection. This task has received significantly less attention in the literature than its supervised counterp

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36

Damilola, Samson. "A Review of Unsupervised Artificial Neural Networks with Applications." International Journal of Computer Applications 181, no. 40 (February 15, 2019): 22–26. http://dx.doi.org/10.5120/ijca2019918425.

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Saunders, Daniel J., Devdhar Patel, Hananel Hazan, Hava T. Siegelmann, and Robert Kozma. "Locally connected spiking neural networks for unsupervised feature learning." Neural Networks 119 (November 2019): 332–40. http://dx.doi.org/10.1016/j.neunet.2019.08.016.

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Ranjard, Louis, and Howard A. Ross. "Unsupervised bird song syllable classification using evolving neural networks." Journal of the Acoustical Society of America 123, no. 6 (June 2008): 4358–68. http://dx.doi.org/10.1121/1.2903861.

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Tagliaferri, R., N. Capuano, and G. Gargiulo. "Automated labeling for unsupervised neural networks: a hierarchical approach." IEEE Transactions on Neural Networks 10, no. 1 (1999): 199–203. http://dx.doi.org/10.1109/72.737509.

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Sun, Yanan, Gary G. Yen, and Zhang Yi. "Evolving Unsupervised Deep Neural Networks for Learning Meaningful Representations." IEEE Transactions on Evolutionary Computation 23, no. 1 (February 2019): 89–103. http://dx.doi.org/10.1109/tevc.2018.2808689.

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Kumar, V. P., and E. S. Manolakos. "Unsupervised statistical neural networks for model-based object recognition." IEEE Transactions on Signal Processing 45, no. 11 (1997): 2709–18. http://dx.doi.org/10.1109/78.650097.

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Dosovitskiy, Alexey, Philipp Fischer, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox. "Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks." IEEE Transactions on Pattern Analysis and Machine Intelligence 38, no. 9 (September 1, 2016): 1734–47. http://dx.doi.org/10.1109/tpami.2015.2496141.

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Storrs, Katherine R., and Roland W. Fleming. "Unsupervised Neural Networks Learn Idiosyncrasies of Human Gloss Perception." Journal of Vision 19, no. 10 (September 6, 2019): 213. http://dx.doi.org/10.1167/19.10.213.

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Barreto, Guilherme de A., and Aluizio F. R. Araújo. "Fast learning of robot trajectories via unsupervised neural networks." IFAC Proceedings Volumes 32, no. 2 (July 1999): 5076–81. http://dx.doi.org/10.1016/s1474-6670(17)56864-0.

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Kosko, B. A. "Structural stability of unsupervised learning in feedback neural networks." IEEE Transactions on Automatic Control 36, no. 7 (July 1991): 785–92. http://dx.doi.org/10.1109/9.85058.

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Likhovidov, Victor. "Variational Approach to Unsupervised Learning Algorithms of Neural Networks." Neural Networks 10, no. 2 (March 1997): 273–89. http://dx.doi.org/10.1016/s0893-6080(96)00051-2.

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Emami, Ebrahim, Touqeer Ahmad, George Bebis, Ara Nefian, and Terry Fong. "Crater Detection Using Unsupervised Algorithms and Convolutional Neural Networks." IEEE Transactions on Geoscience and Remote Sensing 57, no. 8 (August 2019): 5373–83. http://dx.doi.org/10.1109/tgrs.2019.2899122.

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Munoz-Martin, Irene, Stefano Bianchi, Giacomo Pedretti, Octavian Melnic, Stefano Ambrogio, and Daniele Ielmini. "Unsupervised Learning to Overcome Catastrophic Forgetting in Neural Networks." IEEE Journal on Exploratory Solid-State Computational Devices and Circuits 5, no. 1 (June 2019): 58–66. http://dx.doi.org/10.1109/jxcdc.2019.2911135.

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Mishra, Bhabani Shankar Prasad, Om Pandey, Satchidananda Dehuri, and Sung-Bae Cho. "Unsupervised Functional Link Artificial Neural Networks for Cluster Analysis." IEEE Access 8 (2020): 169215–28. http://dx.doi.org/10.1109/access.2020.3024111.

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Wen, C. M., S. L. Hung, C. S. Huang, and J. C. Jan. "Unsupervised fuzzy neural networks for damage detection of structures." Structural Control and Health Monitoring 14, no. 1 (2007): 144–61. http://dx.doi.org/10.1002/stc.116.

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