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Статті в журналах з теми "Memory (Artificial)"
Huang, Guang-qiu. "Artificial memory optimization." Applied Soft Computing 61 (December 2017): 497–526. http://dx.doi.org/10.1016/j.asoc.2017.08.021.
Повний текст джерелаWan, Changjin, Pingqiang Cai, Ming Wang, Yan Qian, Wei Huang, and Xiaodong Chen. "Artificial Sensory Memory." Advanced Materials 32, no. 15 (July 30, 2019): 1902434. http://dx.doi.org/10.1002/adma.201902434.
Повний текст джерелаKim, Dongshin, and Jang-Sik Lee. "Liquid-based memory and artificial synapse." Nanoscale 11, no. 19 (2019): 9726–32. http://dx.doi.org/10.1039/c9nr02767j.
Повний текст джерелаPark, Youngjun, Min-Kyu Kim, and Jang-Sik Lee. "Emerging memory devices for artificial synapses." Journal of Materials Chemistry C 8, no. 27 (2020): 9163–83. http://dx.doi.org/10.1039/d0tc01500h.
Повний текст джерелаWelberg, Leonie. "Artificial activation of a memory trace." Nature Reviews Neuroscience 13, no. 5 (April 13, 2012): 287. http://dx.doi.org/10.1038/nrn3242.
Повний текст джерелаLi, Xianneng, and Guangfei Yang. "Artificial bee colony algorithm with memory." Applied Soft Computing 41 (April 2016): 362–72. http://dx.doi.org/10.1016/j.asoc.2015.12.046.
Повний текст джерелаChen, Yujie, Chi Chen, Hafeez Ur Rehman, Xu Zheng, Hua Li, Hezhou Liu, and Mikael S. Hedenqvist. "Shape-Memory Polymeric Artificial Muscles: Mechanisms, Applications and Challenges." Molecules 25, no. 18 (September 16, 2020): 4246. http://dx.doi.org/10.3390/molecules25184246.
Повний текст джерелаCaravelli, Francesco, Gia-Wei Chern, and Cristiano Nisoli. "Artificial spin ice phase-change memory resistors." New Journal of Physics 24, no. 2 (February 1, 2022): 023020. http://dx.doi.org/10.1088/1367-2630/ac4c0a.
Повний текст джерелаIzawa, Hideki, Yukio Sekiguchi, and Yasuhito Shiota. "The artificial muscle from shape memory alloy." Journal of Life Support Engineering 17, Supplement (2005): 124. http://dx.doi.org/10.5136/lifesupport.17.supplement_124.
Повний текст джерелаQuerlioz, Damien. "(Invited) Memory-Centric Artificial Intelligence with Nanodevices." ECS Meeting Abstracts MA2020-01, no. 24 (May 1, 2020): 1387. http://dx.doi.org/10.1149/ma2020-01241387mtgabs.
Повний текст джерелаДисертації з теми "Memory (Artificial)"
Hedberg, Charlie Forsberg, and Alexander Pedersen. "Artificial Intelligence : Memory-driven decisions in games." Thesis, Blekinge Tekniska Högskola, Institutionen för teknik och estetik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3640.
Повний текст джерелаAtt utveckla AI (Artificiell Intelligence) i spel kan vara en hård och utmanande uppgift. Ibland är det önskvärt att skapa beteenden som följer något sorts logiskt mönster. För att kunna göra detta måste information samlas in och processas. I detta kandidatarbete presenteras en algoritm som kan assistera nuvarande AI-teknologier för att samla in och memorera omgivningsinformation. Denna uppsats täcker också riktlinjer för praktisk implementering fastställda genom undersökning och tester.
Detta är en reflekstionsdel till en digital medieproduktion.
Bachhav, Pramod. "Explicit memory inclusion for efficient artificial bandwidth extension." Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS492.
Повний текст джерелаMost ABE algorithms exploit contextual information or memory captured via the use of static or dynamic features extracted from neighbouring speech frames. The use of memory leads to higher dimensional features and increased computational complexity. When information from look-ahead frames is also utilised, then latency also increases. Past work points toward the benefit to ABE of exploiting memory in the form of dynamic features with a standard regression model. Even so, the literature is missing a quantitative analysis of the relative benefit of explicit memory inclusion. The research presented in this thesis assesses the degree to which explicit memory is of benefit and furthermore reports a number of different techniques that allow for its inclusion without significant increases to latency and computational complexity. Benefits are shown through both a quantitative analysis with an information-theoretic measure and subjective listening tests. Key contributions relate to the preservation of computational efficiency through the use of dimensionality reduction in the form of principal component analysis, semisupervised stacked autoencoders and conditional variational auto-encoders. The two latter techniques optimise dimensionality reduction to deliver superior ABE performance
Kanar, Ege. "Photography as artificial memory: Construction of the Photographic Self." Master's thesis, Akademie múzických umění v Praze. Filmová a televizní fakulta AMU. Knihovna, 2008. http://www.nusl.cz/ntk/nusl-78095.
Повний текст джерелаMoposita, Tatiana. "Artificial Neural Network (ANN) design using Compute-in-Memory." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS682.
Повний текст джерелаNowadays, the era of ”More than Moore” has arisen as a significant influence in light of the limitations anticipated by Moore’s law. The computing systems are exploring alternative technologies to sustain and enhance performance improvements. The idea of alternative innovative technologies has emerged in solving challenges of electronic systems inspired by biological neural networks, commonly referred to as Artificial Neural Network (ANN). The use of emerging non-volatile memory (eNVM) technologies are being explored as promising alternatives. These technologies offer several advantages over traditional CMOS technology, such as increased speed, higher densities, and lower power consumption. As a result, Compute-in-memory employs eNVMs to perform computation within the memory itself, hence increasing memory capacity and processing speed. The objective of this thesis focuses on the research of Artificial Neural Networks design using Compute in Memory, by employing efficient hardware solutions for ANNs at both circuit- and architecture-level. Recent research work in this context has proposed very efficient circuit designs to optimize the enormous computational needs required by data processing by ANNs. Therefore, to explore the capabilities of an ANN at the output node, the design of activation functions were proposed. The selection of an activation function is significant as it determines the power and capabilities of the neural network, and the accuracy of predictions is primarily dependent on this choice. To assess the effectiveness of an activation function designed for analog implementation, the sigmoid and the softmax activation function are proposed. Besides, this thesis explores the integration of emerging memory devices like Spin-Transfer-Torque Magnetic Random Access Memory (STT-MRAM) with CMOS technology. This combined approach aims to leverage the intrinsic capability of in-memory computing offered by these devices. STT-MRAMs based on state-of-the-art perpendicular magnetic tunneling junction (MTJ) and FinFETs has been considered for this study. Single-barrier magnetic tunnel junction (SMTJ) and double-barrier magnetic tunnel junction (DMTJ) devices are considered to evaluate the impact of STT-MRAM cell based on DMTJ against the conventional SMTJ counterpart on the performance of a two-layer multilayer perceptron (MLP) neural network. The assessment was carried out through a customized simulation framework from device and bitcell levels to memory architecture and algorithm levels. Moreover, to improve the energy-efficiency of a Logic-in-Memory (LIM) architecture based on STT-MTJ devices, a new architecture (SIMPLY+) from the Smart Material Implication (SIMPLY) logic and perpendicular MTJ based STT-MRAM technologies was developed. The SIMPLY+ scheme is a promising solution for the development of energy-efficient and reliable in-memory computing architectures. All circuit solutions were evaluated using commercial circuit simulators (e.g. Cadence Virtuoso). Circuit design activity involving emerging memory devices also required the use and calibration of Verilog-A based compact models to integrate the behavior of such devices into the circuit design tool. The solutions presented in this thesis involve techniques that offer significant advancements for future applications. From a design perspective, the integration of logic modules with STT-MRAM memory is highly feasible due to the seamless compatibility between STT-MRAMs and CMOS circuits. This approach not only proves advantageous for standard CMOS technology but also leverages the potential of emerging technologies
Day, Jonathan. ""Must I remember?" : artificial memory systems and early modern England." Thesis, University of Liverpool, 2014. http://livrepository.liverpool.ac.uk/2006202/.
Повний текст джерелаLudwig, Lars [Verfasser], and Thomas [Akademischer Betreuer] Lachmann. "Extended Artificial Memory. Toward an integral cognitive theory of memory and technology / Lars Ludwig. Betreuer: Thomas Lachmann." Kaiserslautern : Technische Universität Kaiserslautern, 2013. http://d-nb.info/1045194794/34.
Повний текст джерелаNtourntoufis, Panayotis. "Aspects of the theory of weightless artificial neural networks." Thesis, Imperial College London, 1994. http://hdl.handle.net/10044/1/8506.
Повний текст джерелаTurvey, Simon Paul. "Analysing and enhancing the performance of associative memory architectures." Thesis, University of Hertfordshire, 2003. http://hdl.handle.net/2299/14113.
Повний текст джерелаTigreat, Philippe. "Sparsity, redundancy and robustness in artificial neural networks for learning and memory." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2017. http://www.theses.fr/2017IMTA0046/document.
Повний текст джерелаThe objective of research in Artificial Intelligence (AI) is to reproduce human cognitive abilities by means of modern computers. The results of the last few years seem to announce a technological revolution that could profoundly change society. We focus our interest on two fundamental cognitive aspects, learning and memory. Associative memories offer the possibility to store information elements and to retrieve them using a sub-part of their content, thus mimicking human memory. Deep Learning allows to transition from an analog perception of the outside world to a sparse and more compact representation.In Chapter 2, we present a neural associative memory model inspired by Willshaw networks, with constrained connectivity. This brings an performance improvement in message retrieval and a more efficient storage of information.In Chapter 3, a convolutional architecture was applied on a task of reading partially displayed words under similar conditions as in a former psychology study on human subjects. This experiment put inevidence the similarities in behavior of the network with the human subjects regarding various properties of the display of words.Chapter 4 introduces a new method for representing categories usingneuron assemblies in deep networks. For problems with a large number of classes, this allows to reduce significantly the dimensions of a network.Chapter 5 describes a method for interfacing deep unsupervised networks with clique-based associative memories
Church, Dana L. "Spatial encoding of artificial flowers by bumblebees (Bombus impatiens): The contents of memory." Thesis, University of Ottawa (Canada), 2005. http://hdl.handle.net/10393/29206.
Повний текст джерелаКниги з теми "Memory (Artificial)"
Kanerva, Pentti. Sparse distributed memory. Cambridge, Mass: MIT Press, 1988.
Знайти повний текст джерелаKolcz, A. Approximation properties of memory-based artificial neural networks. Manchester: UMIST, 1995.
Знайти повний текст джерелаBarba, Gianfranco Dalla. Memory, Consciousness and Temporality. Boston, MA: Springer US, 2002.
Знайти повний текст джерелаKanerva, Pentti. Sparse distributed memory and related models. [Moffett Field, Calif.?]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1992.
Знайти повний текст джерелаMace, Mary E. Memory Storage Patterns in Parallel Processing. Boston, MA: Springer US, 1987.
Знайти повний текст джерелаDenning, Peter J. Sparse disributed memory. [Moffett Field, Calif.?]: Research Institute for Advanced Computer Science, [NASA Ames Research Center, 1989.
Знайти повний текст джерелаJean, Delacour, and Levy Jean-Claude, eds. Systems with learning and memory abilities. Amsterdam: North-Holland, 1988.
Знайти повний текст джерелаK, Riesbeck Christopher, ed. Experience, memory, and reasoning. Hillsdale, N.J: L. Erlbaum Associates, 1986.
Знайти повний текст джерелаJorgensen, Charles C. Distributed memory approaches for robotic neural controllers. [Moffett Field, Calif.?]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1990.
Знайти повний текст джерелаPrzybylski, Steven A. Cache and memory hierarchy design: A performance-directed approach. San Mateo, Calif: Morgan Kaufmann Publishers, 1990.
Знайти повний текст джерелаЧастини книг з теми "Memory (Artificial)"
Pratt, Ian. "Memory Organization." In Artificial Intelligence, 140–60. London: Macmillan Education UK, 1994. http://dx.doi.org/10.1007/978-1-349-13277-5_7.
Повний текст джерелаCrowder, James A., John N. Carbone, and Shelli A. Friess. "Artificial Memory Systems." In Artificial Cognition Architectures, 53–78. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8072-3_5.
Повний текст джерелаKrauss, Patrick. "Memory." In Artificial Intelligence and Brain Research, 59–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2024. http://dx.doi.org/10.1007/978-3-662-68980-6_7.
Повний текст джерелаIsaev, Peter, and Patrick Hammer. "Memory System and Memory Types for Real-Time Reasoning Systems." In Artificial General Intelligence, 147–57. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33469-6_15.
Повний текст джерелаJia, Huijue. "Memory in Cells." In Neuroscience for Artificial Intelligence, 59–83. New York: Jenny Stanford Publishing, 2023. http://dx.doi.org/10.1201/9781003410980-4.
Повний текст джерелаÖzkural, Eray. "Towards Heuristic Algorithmic Memory." In Artificial General Intelligence, 382–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22887-2_47.
Повний текст джерелаWichert, Andreas. "Quantum Associative Memory." In Quantum Artificial Intelligence with Qiskit, 183–94. Boca Raton: Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003374404-14.
Повний текст джерелаOrseau, Laurent, and Mark Ring. "Memory Issues of Intelligent Agents." In Artificial General Intelligence, 219–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35506-6_23.
Повний текст джерелаEdelkamp, Stefan. "Memory Limitations in Artificial Intelligence." In Algorithms for Memory Hierarchies, 233–50. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36574-5_11.
Повний текст джерелаJia, Huijue. "Memory in Dendritic Spines." In Neuroscience for Artificial Intelligence, 85–112. New York: Jenny Stanford Publishing, 2023. http://dx.doi.org/10.1201/9781003410980-5.
Повний текст джерелаТези доповідей конференцій з теми "Memory (Artificial)"
Dudzik, Bernd, Hayley Hung, Mark Neerincx, and Joost Broekens. "Artificial Empathic Memory." In the 2018 Workshop. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3267799.3267801.
Повний текст джерелаAe, T., R. Aibara, and Y. Nishioka. "A memory-based artificial neural network." In 1991 IEEE International Joint Conference on Neural Networks. IEEE, 1991. http://dx.doi.org/10.1109/ijcnn.1991.170468.
Повний текст джерелаShimizu, Y., and Y. Osana. "Chaotic Complex-Valued Multidirectional Associative Memory." In Artificial Intelligence and Applications. Calgary,AB,Canada: ACTAPRESS, 2010. http://dx.doi.org/10.2316/p.2010.674-121.
Повний текст джерелаAdda, Coline, Julien Tranchant, Pablo Stoliar, Benoit Corraze, Etienne Janod, Ralph Gay, Roger Llopis, Marie-Paule Besland, Luis E. Hueso, and Laurent Cario. "An Artificial Neuron Founded on Resistive Switching of Mott Insulators." In 2017 IEEE International Memory Workshop (IMW). IEEE, 2017. http://dx.doi.org/10.1109/imw.2017.7939071.
Повний текст джерелаSun, Koun-Tem, Syuan-Rong Syu, and Shih-Yun Lee. "Design Semantic Memory by Artificial Neural Network." In 2019 IEEE 2nd International Conference on Knowledge Innovation and Invention (ICKII). IEEE, 2019. http://dx.doi.org/10.1109/ickii46306.2019.9042636.
Повний текст джерелаAlexandrino, Jose Lima, Cleber Zanchettin, and Edson Costa de Barros Carvalho Filho. "Artificial Immune System with ART Memory Hibridization." In 7th International Conference on Hybrid Intelligent Systems (HIS 2007). IEEE, 2007. http://dx.doi.org/10.1109/ichis.2007.4344028.
Повний текст джерелаSheri, Bhagia, Pooja Kumari, Isma Farah Siddiqui, and Hira Noman. "Artificial Intelligence Based Memory Stash Alzheimer’s Aid." In 2020 International Conference on Information Science and Communication Technology (ICISCT). IEEE, 2020. http://dx.doi.org/10.1109/icisct49550.2020.9080030.
Повний текст джерелаM. Vogl, Thomas. "Artificial Intelligence and Organizational Memory in Government." In dg.o '20: The 21st Annual International Conference on Digital Government Research. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3396956.3396971.
Повний текст джерелаAlexandrino, Jose Lima, Cleber Zanchettin, and Edson Costa de Barros Carvalho Filho. "Artificial Immune System with ART Memory Hibridization." In 7th International Conference on Hybrid Intelligent Systems (HIS 2007). IEEE, 2007. http://dx.doi.org/10.1109/his.2007.47.
Повний текст джерелаBoybat, Irem, Manuel Le Gallo, S. R. Nandakumar, Timoleon Moraitis, Tomas Tuma, Bipin Rajendran, Yusuf Leblebici, Abu Sebastian, and Evangelos Eleftheriou. "An efficient synaptic architecture for artificial neural networks." In 2017 17th Non-Volatile Memory Technology Symposium (NVMTS). IEEE, 2017. http://dx.doi.org/10.1109/nvmts.2017.8171302.
Повний текст джерелаЗвіти організацій з теми "Memory (Artificial)"
Walden, Victoria Grace, and Kate Marrison, eds. Recommendations for using Artificial Intelligence and Machine Learning for Holocaust Memory and Education. REFRAME, January 2023. http://dx.doi.org/10.20919/elvh8804.
Повний текст джерелаBARKHATOV, NIKOLAY, and SERGEY REVUNOV. A software-computational neural network tool for predicting the electromagnetic state of the polar magnetosphere, taking into account the process that simulates its slow loading by the kinetic energy of the solar wind. SIB-Expertise, December 2021. http://dx.doi.org/10.12731/er0519.07122021.
Повний текст джерелаKhan, Saif M. Maintaining the AI Chip Competitive Advantage of the United States and its Allies. Center for Security and Emerging Technology, December 2019. http://dx.doi.org/10.51593/20190013.
Повний текст джерелаChopra, H. D. FINAL REPORT: FG02-01ER-45906 - A novel class of artificially modulated magnetic multilayers based on magnetic shape memory alloys. Office of Scientific and Technical Information (OSTI), June 2005. http://dx.doi.org/10.2172/840960.
Повний текст джерелаEngel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
Повний текст джерелаAroca Moya, Belén. Conceptos, fundamentos y herramientas de neurociencia y su aplicación al billete. Madrid: Banco de España, February 2023. http://dx.doi.org/10.53479/29749.
Повний текст джерелаMemoria de Iniciación Científica 2023. Universidad Autónoma de Chile, March 2024. http://dx.doi.org/10.32457/12728/10296202484.
Повний текст джерела