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Добірка наукової літератури з теми "Hidden units"

Автор: Grafiati

Опубліковано: 3 червня 2025

Оновлено: 31 липня 2025

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Статті в журналах з теми "Hidden units"

1

Tyrcha, Joanna, and John Hertz. "Network inference with hidden units." Mathematical Biosciences and Engineering 11, no. 1 (2014): 149–65. http://dx.doi.org/10.3934/mbe.2014.11.149.

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2

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

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Анотація:

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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3

Côté, Marc-Alexandre, and Hugo Larochelle. "An Infinite Restricted Boltzmann Machine." Neural Computation 28, no. 7 (2016): 1265–88. http://dx.doi.org/10.1162/neco_a_00848.

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Анотація:

We present a mathematical construction for the restricted Boltzmann machine (RBM) that does not require specifying the number of hidden units. In fact, the hidden layer size is adaptive and can grow during training. This is obtained by first extending the RBM to be sensitive to the ordering of its hidden units. Then, with a carefully chosen definition of the energy function, we show that the limit of infinitely many hidden units is well defined. As with RBM, approximate maximum likelihood training can be performed, resulting in an algorithm that naturally and adaptively adds trained hidden uni

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4

Setiono, Rudy. "Feedforward Neural Network Construction Using Cross Validation." Neural Computation 13, no. 12 (2001): 2865–77. http://dx.doi.org/10.1162/089976601317098565.

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This article presents an algorithm that constructs feedforward neural networks with a single hidden layer for pattern classification. The algorithm starts with a small number of hidden units in the network and adds more hidden units as needed to improve the network's predictive accuracy. To determine when to stop adding new hidden units, the algorithm makes use of a subset of the available training samples for cross validation. New hidden units are added to the network only if they improve the classification accuracy of the network on the training samples and on the cross-validation samples. E

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5

Luo, Heng, Ruimin Shen, Changyong Niu, and Carsten Ullrich. "Sparse Group Restricted Boltzmann Machines." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 429–34. http://dx.doi.org/10.1609/aaai.v25i1.7923.

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Анотація:

Since learning in Boltzmann machines is typically quite slow, there is a need to restrict connections within hidden layers. However, theresulting states of hidden units exhibit statistical dependencies. Based on this observation, we propose using l1/l2 regularization upon the activation probabilities of hidden units in restricted Boltzmann machines to capture the local dependencies among hidden units. This regularization not only encourages hidden units of many groups to be inactive given observed data but also makes hidden units within a group compete with each other for modeling observed dat

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6

Zemel, Richard S., and Geoffrey E. Hinton. "Learning Population Codes by Minimizing Description Length." Neural Computation 7, no. 3 (1995): 549–64. http://dx.doi.org/10.1162/neco.1995.7.3.549.

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Анотація:

The minimum description length (MDL) principle can be used to train the hidden units of a neural network to extract a representation that is cheap to describe but nonetheless allows the input to be reconstructed accurately. We show how MDL can be used to develop highly redundant population codes. Each hidden unit has a location in a low-dimensional implicit space. If the hidden unit activities form a bump of a standard shape in this space, they can be cheaply encoded by the center of this bump. So the weights from the input units to the hidden units in an autoencoder are trained to make the ac

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7

BLATT, MARCELO, EYTAN DOMANY, and IDO KANTER. "ON THE EQUIVALENCE OF TWO-LAYERED PERCEPTRONS WITH BINARY NEURONS." International Journal of Neural Systems 06, no. 03 (1995): 225–31. http://dx.doi.org/10.1142/s0129065795000160.

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We consider two-layered perceptrons consisting of N binary input units, K binary hidden units and one binary output unit, in the limit N≫K≥1. We prove that the weights of a regular irreducible network are uniquely determined by its input-output map up to some obvious global symmetries. A network is regular if its K weight vectors from the input layer to the K hidden units are linearly independent. A (single layered) perceptron is said to be irreducible if its output depends on every one of its input units; and a two-layered perceptron is irreducible if the K+1 perceptrons that constitute such

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8

Morral, J. E. "Chemical Diffusivities and Their Hidden Concentration Units." Journal of Phase Equilibria and Diffusion 35, no. 5 (2014): 581–86. http://dx.doi.org/10.1007/s11669-014-0308-8.

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9

Williams, Christopher K. I. "Computation with Infinite Neural Networks." Neural Computation 10, no. 5 (1998): 1203–16. http://dx.doi.org/10.1162/089976698300017412.

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Анотація:

For neural networks with a wide class of weight priors, it can be shown that in the limit of an infinite number of hidden units, the prior over functions tends to a gaussian process. In this article, analytic forms are derived for the covariance function of the gaussian processes corresponding to networks with sigmoidal and gaussian hidden units. This allows predictions to be made efficiently using networks with an infinite number of hidden units and shows, somewhat paradoxically, that it may be easier to carry out Bayesian prediction with infinite networks rather than finite ones.

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10

Prasetyo, Simeon Yuda. "Prediksi Gagal Jantung Menggunakan Artificial Neural Network." Jurnal SAINTEKOM 13, no. 1 (2023): 79–88. http://dx.doi.org/10.33020/saintekom.v13i1.379.

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Анотація:

Cardiovascular disease or heart problems are the leading cause of death worldwide. According to WHO (World Health Organization) every year there are more than 17.9 million deaths worldwide. In previous studies, there have been many studies related to the application of machine learning to predict heart failure and obtained quite good results, ranging from 85 percent to 90 percent, with sophisticated models optimized using neural networks. In this research, experiments were carried out using similar architectures based on the state of the art from previous research, namely Artificial Neural Net

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Дисертації з теми "Hidden units"

1

Nafa, Fatema B. "DISCOVERING HIDDEN COGNITIVE SKILL DEPENDENCIES BETWEEN KNOWLEDGE UNITS USING MARKOV COGNITIVE KNOWLEDGE STATE NETWORK (MCKSN)." Kent State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=kent1553688024482058.

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2

Inoue, Masato. "On-line learning theory of soft committee machines with correlated hidden units : Steepest gradient descent and natural gradient descent." Kyoto University, 2003. http://hdl.handle.net/2433/148746.

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3

Hähne, Hauke [Verfasser], Joachim [Akademischer Betreuer] Peinke, Pedro [Akademischer Betreuer] Lind, and Alexander [Akademischer Betreuer] Hartmann. "Propagation of fluctuations and detection of hidden units in network dynamical systems / Hauke Hähne ; Joachim Peinke, Pedro Lind, Alexander Hartmann." Oldenburg : BIS der Universität Oldenburg, 2019. http://d-nb.info/1197228861/34.

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4

Ghasemi, Ghaleh Bahmani Afsaneh. "Machine perception of human emotions using motion patterns." Thesis, Queensland University of Technology, 2017. https://eprints.qut.edu.au/104297/1/Afsaneh_Ghasemi%20Ghaleh%20Bahmani_Thesis.pdf.

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We propose novel, intelligent facial expression recognition systems as well as unique encoding of the dynamics of facial actions, enabling machines to have the ability to sense and understand human visual interaction signals. Various methodologies have been proposed to address the existing limitations in performance as well as the computing resource constraints, to advance the field of automatic human emotion recognition.

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5

O'Leary, Stephen. "'Hidden voices' : an exploratory single case study into the multiple worlds of a 15 year old young man with autism." Thesis, University of Exeter, 2011. http://hdl.handle.net/10036/3700.

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This thesis presents a 31-day case study carried out with a 15 year old young man who has classical autism. The study involved introducing him to a number of new and challenging activities, in a variety of contexts, over 31 days, that were previously assumed to be outside of his range of capability. The case study found that the application of the concepts of choice, control, challenge and risk had an unexpectedly positive impact upon the young man’s performance. This study further attempts to explore the concept of narrative as a ‘pedagogical bridge’ between the ‘worlds’ of autism and neuroty

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6

Panahandeh, Ghazaleh, Nasser Mohammadiha, Arne Leijon, and Peter Händel. "Continuous Hidden Markov Model for Pedestrian Activity Classification and Gait Analysis." KTH, Signalbehandling, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-107713.

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This paper presents a method for pedestrian activity classification and gait analysis based on the microelectromechanical-systems inertial measurement unit (IMU). The work targets two groups of applications, including the following: 1) human activity classification and 2) joint human activity and gait-phase classification. In the latter case, the gait phase is defined as a substate of a specific gait cycle, i.e., the states of the body between the stance and swing phases. We model the pedestrian motion with a continuous hidden Markov model (HMM) in which the output density functions are assume

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7

Kim, Woojae. "Understanding the connectionist modeling of quasiregular mappings in reading aloud." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1171994549.

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8

Li, Haoyu. "Recent hidden Markov models for lower limb locomotion activity detection and recognition using IMU sensors." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEC041.

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Анотація:

Le contexte de la thèse est celui du quantified-self, un mouvement né en Californie qui consiste à mieux se connaître en mesurant les données relatives à son corps et à ses activités. Les travaux de recherche ont consisté à développer des algorithmes d'analyse des signaux d'un capteur IMU (\textit{Inertial Measurement Unit}) placé sur la jambe pour reconnaître différentes activités de mouvement telles que la marche, la course, la montée d’escalier.... Ces activités sont reconnaissables grâce à la forme des signaux d'accélération et de vitesse angulaire du capteur, tous triaxiaux, pendant le mo

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9

Ridall, Peter Gareth. "Bayesian Latent Variable Models for Biostatistical Applications." Thesis, Queensland University of Technology, 2004. https://eprints.qut.edu.au/16164/1/Peter_Ridall_Thesis.pdf.

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In this thesis we develop several kinds of latent variable models in order to address three types of bio-statistical problem. The three problems are the treatment effect of carcinogens on tumour development, spatial interactions between plant species and motor unit number estimation (MUNE). The three types of data looked at are: highly heterogeneous longitudinal count data, quadrat counts of species on a rectangular lattice and lastly, electrophysiological data consisting of measurements of compound muscle action potential (CMAP) area and amplitude. Chapter 1 sets out the structure and th

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10

Ridall, Peter Gareth. "Bayesian Latent Variable Models for Biostatistical Applications." Queensland University of Technology, 2004. http://eprints.qut.edu.au/16164/.

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Анотація:

In this thesis we develop several kinds of latent variable models in order to address three types of bio-statistical problem. The three problems are the treatment effect of carcinogens on tumour development, spatial interactions between plant species and motor unit number estimation (MUNE). The three types of data looked at are: highly heterogeneous longitudinal count data, quadrat counts of species on a rectangular lattice and lastly, electrophysiological data consisting of measurements of compound muscle action potential (CMAP) area and amplitude. Chapter 1 sets out the structure and th

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Книги з теми "Hidden units"

1

Ōkawa, Ryūhō. South Korea's conspiracy: President Park's hidden agenda to unite with China. IRH Press Co., Ltd, 2013.

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2

Engineers, Anderson Consulting. Final report for Hidden Valley pipeline level II feasibility study. Anderson Consulting Engineers, 2003.

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3

Glenn, Greenwald. No place to hide: Edward Snowden, the NSA and the surveillance state. Metropolitan Books, Henry Holt and Company, 2014.

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4

Glenn, Greenwald. No place to hide: Edward Snowden, the NSA, and the U.S. surveillance state. Signal, 2015.

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5

Perlot, Martin. The suppression of learning at the hidden units of neural networks. 1989.

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6

Hidden Unity. Lang Publishing, Incorporated, Peter, 2014.

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7

Hidden Footprints of Unity. Sid Harta Publishers, 2006.

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8

Taylor, John C. Hidden Unity in Nature's Laws. Cambridge University Press, 2005.

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9

Taylor, John C. Hidden Unity in Nature's Laws. Cambridge University Press, 2001.

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10

Taylor, John C. Hidden Unity in Nature's Laws. Cambridge University Press, 2010.

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Частини книг з теми "Hidden units"

1

Crain, Stephen, Takuya Goro, and Utako Minai. "14. Hidden units in child language." In Studies in Language Companion Series. John Benjamins Publishing Company, 2007. http://dx.doi.org/10.1075/slcs.92.16cra.

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2

Tomczak, Jakub M., and Adam Gonczarek. "Sparse hidden units activation in Restricted Boltzmann Machine." In Progress in Systems Engineering. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08422-0_27.

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3

Cao, Binbin, Jianmin Li, Jun Wu, and Bo Zhang. "Restricted Boltzmann Machine with Adaptive Local Hidden Units." In Neural Information Processing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42042-9_39.

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4

Hu, Zhen, Wenzheng Hu, and Changshui Zhang. "Training Deep Belief Network with Sparse Hidden Units." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45646-0_2.

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5

Kurokawa, Hiroaki, Chun-Ying Ho, and Shinsaku Mori. "A Novel Back Propagation Algorithm with Optimal Number of Hidden Units." In ICANN ’93. Springer London, 1993. http://dx.doi.org/10.1007/978-1-4471-2063-6_222.

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6

Arai, Masahiko. "Conditions on Activation Functions of Hidden Units for Learning by Backpropagation." In International Neural Network Conference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_119.

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7

Ying, Xingren. "Linearly Independent Transformation by Hidden Units with Analytic Asymptotic Activation Function." In International Neural Network Conference. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-009-0643-3_153.

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8

Pierucci, Piero, Alessandro Falaschi, and Massimo Giustiniani. "Phonetic Units and Phonotactical Structure Inference by Ergodic Hidden Markov Models." In Speech Recognition and Understanding. Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-76626-8_6.

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9

Berglund, Mathias, Tapani Raiko, and KyungHyun Cho. "Measuring the Usefulness of Hidden Units in Boltzmann Machines with Mutual Information." In Neural Information Processing. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42054-2_60.

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10

Ding, Chunmei, Feilong Cao, and Zongben Xu. "The Essential Approximation Order for Neural Networks with Trigonometric Hidden Layer Units." In Advances in Neural Networks - ISNN 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11759966_11.

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Тези доповідей конференцій з теми "Hidden units"

1

Alba-Robles, Emilio, Oscar Daniel Lara-Monta�o, Fernando Israel G�mez-Castro, Jahaziel Alberto S�nchez-G�mez, and Manuel Toledano-Ayala. "Modelling of a Propylene Glycol Production Process With Artificial Neural Networks: Optimization of the Architecture." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.139694.

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Chemical process models often involve high non-linearity due to thermodynamic and kinetic relationships, with non-convex bilinear terms adding complexity to process optimization. Recently, data-driven models, particularly artificial neural networks (ANNs), have gained traction for representing chemical processing units. The predictive accuracy of ANNs depends on data quality, variable interactions, and network architecture, the latter being an optimization challenge itself. This study proposes and evaluates two strategies to optimize ANN architecture for modeling a propylene glycol production

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2

Ruiz, Adria, Joost Van de Weijer, and Xavier Binefa. "From Emotions to Action Units with Hidden and Semi-Hidden-Task Learning." In 2015 IEEE International Conference on Computer Vision (ICCV). IEEE, 2015. http://dx.doi.org/10.1109/iccv.2015.422.

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3

Satoh, Seiya, and Ryohei Nakano. "Singularity Stairs Following with Limited Numbers of Hidden Units." In International Conference on Neural Computation Theory and Applications. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0005075601800186.

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4

Hirose, Yamashita, and Hijiya. "Backpropagation algorithm which varies the number of hidden units." In International Joint Conference on Neural Networks. IEEE, 1989. http://dx.doi.org/10.1109/ijcnn.1989.118518.

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5

Weigend, A. S., and D. E. Rumelhart. "The effective dimension of the space of hidden units." In 1991 IEEE International Joint Conference on Neural Networks. IEEE, 1991. http://dx.doi.org/10.1109/ijcnn.1991.170692.

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6

Mantena, Gautam, and Khe Chai Sim. "Entropy-based pruning of hidden units to reduce DNN parameters." In 2016 IEEE Spoken Language Technology Workshop (SLT). IEEE, 2016. http://dx.doi.org/10.1109/slt.2016.7846335.

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7

Shatalova, Lyudmila. "HIDDEN RESERVES OF PRECEDENT UNITS IN MODERN RUSSIAN MEDIA TEXTS." In 5th SGEM International Multidisciplinary Scientific Conferences on SOCIAL SCIENCES and ARTS SGEM2018. STEF92 Technology, 2018. http://dx.doi.org/10.5593/sgemsocial2018/3.6/s14.033.

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8

Peterson, Carsten, and Steve Redfield. "Adaptive Learning With Hidden Units Using A Single Photorefractive Crystal." In Optical Computing '88, edited by Pierre H. Chavel, Joseph W. Goodman, and Gerard Roblin. SPIE, 1989. http://dx.doi.org/10.1117/12.947928.

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9

Kurita, T., H. Asoh, S. Umeyama, S. Akaho, and A. Hosomi. "A structural learning by adding independent noises to hidden units." In Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94). IEEE, 1994. http://dx.doi.org/10.1109/icnn.1994.374174.

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10

Plannerer, B., and G. Ruske. "Recognition of demisyllable based units using semicontinuous hidden Markov models." In [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1992. http://dx.doi.org/10.1109/icassp.1992.225842.

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Звіти організацій з теми "Hidden units"

1

Breckon, Jonathan, Sarah Chaytor, Alison Clarke, Hannah Durrant, and Olivia Stevenson. Hidden Talents: Core Competencies of Knowledge Mobilisers Working in Public Policy. UCL Public Policy, 2025. https://doi.org/10.14324/000.rp.10207672.

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Evidence is essential for effective policymaking at all levels of government. Connecting decisionmakers to the wealth of research generated by universities is critical to ensuring that they can access, interpret and apply that evidence to inform policy development and decisions. In the UK, this requires universities to pay attention to the ways in which they can connect their research knowledge to public policymaking. In many cases, such ‘academic-policy engagement’ is undertaken by ‘third space professionals’ (Whitchurch, 2008, 2013) working at the interface between academic research and publ

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2

Prysyazhna-Gapchenko, Julia. Еміграційні видання для селян: між фаховістю і політикою. Ivan Franko National University of Lviv, 2023. http://dx.doi.org/10.30970/vjo.2023.52-53.11720.

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In the article rare editions of magazine type are first probed for peasants which nursed in an environment the Ukrainian emigrants in the first post-war years on territory of the American area of occupation in Germany, and also in the USA. Separately paid regard to mision role of magazines in the association of the nebulized peasants round a desire to apply the obtained previous experience and knowledge on strange land, to present the world the Ukrainian peasantry as labour productive force and also round the idea of fight for independence, joining in with political activity of «old» parties a

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