Gotowe bibliografie tematyczne / Neural fields

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  1. Artykuły w czasopismach
  2. Rozprawy doktorskie
  3. Książki
  4. Części książek
  5. Streszczenia konferencji
  6. Raporty organizacyjne

Gotowa bibliografia na temat „Neural fields”

Autor: Grafiati
Data publikacji: 4 czerwca 2021
Data aktualizacji: 8 czerwca 2024

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Artykuły w czasopismach na temat "Neural fields"

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Coombes, Stephen. "Neural fields." Scholarpedia 1, no. 6 (2006): 1373. http://dx.doi.org/10.4249/scholarpedia.1373.

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Aigerman, Noam, Kunal Gupta, Vladimir G. Kim, Siddhartha Chaudhuri, Jun Saito, and Thibault Groueix. "Neural jacobian fields." ACM Transactions on Graphics 41, no. 4 (July 2022): 1–17. http://dx.doi.org/10.1145/3528223.3530141.

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This paper introduces a framework designed to accurately predict piecewise linear mappings of arbitrary meshes via a neural network, enabling training and evaluating over heterogeneous collections of meshes that do not share a triangulation, as well as producing highly detail-preserving maps whose accuracy exceeds current state of the art. The framework is based on reducing the neural aspect to a prediction of a matrix for a single given point, conditioned on a global shape descriptor. The field of matrices is then projected onto the tangent bundle of the given mesh, and used as candidate jaco
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Smaragdis, Paris. "Neural acoustic fields." Journal of the Acoustical Society of America 153, no. 3_supplement (March 1, 2023): A175. http://dx.doi.org/10.1121/10.0018569.

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We present a way to compactly represent acoustic transfer functions using a small, yet flexible, parametric representation. We show that we can use a neural network as a “soft” table lookup and train it to produce the value of transfer functions at arbitrary points in space and time. Doing so allows us to interpolate and produce unseen data, and to represent acoustic environments using a remarkably compact representation. Due to this representation being differentiable, this opens up multiple opportunities to employ such models within more sophisticated audio processing systems.
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Friston, Karl. "Mean-Fields and Neural Masses." PLoS Computational Biology 4, no. 8 (August 29, 2008): e1000081. http://dx.doi.org/10.1371/journal.pcbi.1000081.

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Chappet De Vangel, Benoît, Cesar Torres-huitzil, and Bernard Girau. "Randomly Spiking Dynamic Neural Fields." ACM Journal on Emerging Technologies in Computing Systems 11, no. 4 (April 27, 2015): 1–26. http://dx.doi.org/10.1145/2629517.

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Igel, Christian, Wolfram Erlhagen, and Dirk Jancke. "Optimization of dynamic neural fields." Neurocomputing 36, no. 1-4 (February 2001): 225–33. http://dx.doi.org/10.1016/s0925-2312(00)00328-3.

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Belhe, Yash, Michaël Gharbi, Matthew Fisher, Iliyan Georgiev, Ravi Ramamoorthi, and Tzu-Mao Li. "Discontinuity-Aware 2D Neural Fields." ACM Transactions on Graphics 42, no. 6 (December 5, 2023): 1–11. http://dx.doi.org/10.1145/3618379.

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Neural image representations offer the possibility of high fidelity, compact storage, and resolution-independent accuracy, providing an attractive alternative to traditional pixel- and grid-based representations. However, coordinate neural networks fail to capture discontinuities present in the image and tend to blur across them; we aim to address this challenge. In many cases, such as rendered images, vector graphics, diffusion curves, or solutions to partial differential equations, the locations of the discontinuities are known. We take those locations as input, represented as linear, quadra
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Esselle, K. P., and M. A. Stuchly. "Neural stimulation with magnetic fields: analysis of induced electric fields." IEEE Transactions on Biomedical Engineering 39, no. 7 (July 1992): 693–700. http://dx.doi.org/10.1109/10.142644.

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Bressloff, Paul C., and Matthew A. Webber. "Front Propagation in Stochastic Neural Fields." SIAM Journal on Applied Dynamical Systems 11, no. 2 (January 2012): 708–40. http://dx.doi.org/10.1137/110851031.

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Kilpatrick, Zachary P., and Grégory Faye. "Pulse Bifurcations in Stochastic Neural Fields." SIAM Journal on Applied Dynamical Systems 13, no. 2 (January 2014): 830–60. http://dx.doi.org/10.1137/140951369.

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Rozprawy doktorskie na temat "Neural fields"

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Ueda, Hiroyuki. "Studies on low-field functional MRI to detect tiny neural magnetic fields." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263666.

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付記する学位プログラム名: 京都大学卓越大学院プログラム「先端光・電子デバイス創成学」<br>京都大学<br>新制・課程博士<br>博士(工学)<br>甲第23205号<br>工博第4849号<br>京都大学大学院工学研究科電気工学専攻<br>(主査)教授 小林 哲生, 教授 松尾 哲司, 特定教授 中村 武恒<br>学位規則第4条第1項該当<br>Doctor of Philosophy (Engineering)<br>Kyoto University<br>DFAM
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Webber, Matthew. "Stochastic neural field models of binocular rivalry waves." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:c444a73e-20e3-454d-85ae-bbc8831fdf1f.

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Binocular rivalry is an interesting phenomenon where perception oscillates between different images presented to the two eyes. This thesis is primarily concerned with modelling travelling waves of visual perception during transitions between these perceptual states. In order to model this effect in such a way that we retain as much analytical insight into the mechanisms as possible we employed neural field theory. That is, rather than modelling individual neurons in a neural network we treat the cortical surface as a continuous medium and establish integro-differential equations for the activi
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Davenport, Christopher M. "Neural circuitry of retinal receptive fields in primate /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/10652.

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Arocena, Miguel. "Control of neural stem cell migration by electric fields." Thesis, University of Aberdeen, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540498.

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Neural stem cells showed strong electrotaxis, evidenced by highly directed migration towards the cathode.  Optimal electrotaxis was found to require growth factors and phosphoinositide 3-kinase (PI3-K) signalling, although reduced electrotaxis could be obtained without growth factors at the highest EFs used.  After EF exposure, neural stem cell trajectories became much more linear, and a reduction in the number of cell protrusions oriented towards the anode was observed.  Also, protrusions initially orienting towards the cathode retracted after the polarity of the EF was reversed, suggesting t
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Ferguson, Archibald Stewart. "Theoretical calculation of magnetic fields generated by neural currents." Case Western Reserve University School of Graduate Studies / OhioLINK, 1991. http://rave.ohiolink.edu/etdc/view?acc_num=case1055524502.

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Qi, Yang. "Anomalous neural pattern dynamics: formation mechanisms and functional roles." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/18808.

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Spatiotemporal activity patterns with complex dynamics have been widely observed in the cortex, but their formation mechanisms and functional roles remain unclear. In this thesis, we first analyze how interactions of distributed bump activity patterns give rise to anomalous subdiffusive dynamics. Unlike normal diffusion whose mean squared error in representing working memory increases linearly with time, subdiffusion is characterized by a sublinear increase in its error, thereby significantly reducing memory degradation. The computational role of subdiffusive pattern dynamics in working memory
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Rohlén, Andreas. "UAV geolocalization in Swedish fields and forests using Deep Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300390.

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The ability for unmanned autonomous aerial vehicles (UAV) to localize themselves in an environment is fundamental for them to be able to function, even if they do not have access to a global positioning system. Recently, with the success of deep learning in vision based tasks, there have been some proposed methods for absolute geolocalization using vison based deep learning with satellite and UAV images. Most of these are only tested in urban environments, which begs the question: How well do they work in non-urban areas like forests and fields? One drawback of deep learning is that models are
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Curtis, Maurice A. "Neural progenitor cells in the Huntington's Disease human brain." Thesis, University of Auckland, 2004. http://hdl.handle.net/2292/3114.

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The recent demonstration of endogenous progenitor cells in the adult mammalian brain raises the exciting possibility that these undifferentiated cells may be able to generate new neurons for cell replacement in diseases such as Huntington's disease (HD). Previous studies have shown that neural stem cells in the rodent brain subependymal layer (SEL), adjacent to the caudate nucleus, proliferate and differentiate into neurons and glial cells but no previous study has characterised the human SEL or shown neurogenesis in the diseased human brain. In this study, histochemical and immunohistochemica
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Zhang, Yiming. "Applications of artificial neural networks (ANNs) in several different materials research fields." Thesis, Queen Mary, University of London, 2010. http://qmro.qmul.ac.uk/xmlui/handle/123456789/362.

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In materials science, the traditional methodological framework is the identification of the composition-processing-structure-property causal pathways that link hierarchical structure to properties. However, all the properties of materials can be derived ultimately from structure and bonding, and so the properties of a material are interrelated to varying degrees. The work presented in this thesis, employed artificial neural networks (ANNs) to explore the correlations of different material properties with several examples in different fields. Those including 1) to verify and quantify known corr
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Harris, William H. (William Hunt). "Machine learning transferable physics-based force fields using graph convolutional neural networks." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/128979.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 2020<br>Cataloged from student-submitted PDF of thesis.<br>Includes bibliographical references (pages 22-24).<br>Molecular dynamics and Monte Carlo methods allow the properties of a system to be determined from its potential energy surface (PES). In the domain of crystalline materials, the PES is needed for electronic structure calculations, critical for modeling semiconductors, optical, and energy-storage materials. While first principles techniques can be used to obtain the PES to high accur
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Książki na temat "Neural fields"

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Coombes, Stephen, Peter beim Graben, Roland Potthast, and James Wright, eds. Neural Fields. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1.

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1919-, Pribram Karl H., and Eccles, John C. Sir, 1903-, eds. Rethinking neural networks: Quantum fields and biological data. Hillsdale, N.J: Erlbaum, 1993.

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B, Pinter Robert, and Nabet Bahram, eds. Nonlinear vision: Determination of neural receptive fields, function, and networks. Boca Raton: CRC Press, 1992.

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Kozma, Robert, and Walter J. Freeman. Cognitive Phase Transitions in the Cerebral Cortex - Enhancing the Neuron Doctrine by Modeling Neural Fields. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24406-8.

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Horowitz, John. The effects of hypergravic fields on neural signalling in the hippocampus. [Washington, DC: National Aeronautics and Space Administration, 1991.

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Booth, John Nicholas. The application of weak complex magnetic fields on the neural correlates of consciousness. Sudbury, Ont: Laurentian University, School of Graduate Studies, 2006.

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1919-, Pribram Karl H., and Eccles, John C. Sir, 1903-, eds. Rethinking neural networks: Quantum fields and biological data : proceedings of the First Appalachian Conference on Behavioral Neurodynamics. Hillsdale, N.J: Erlbaum, 1993.

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Center, Ames Research, ed. Cascading a systolic array and a feedforward neural network for navigation and obstacle avoidance using potential fields. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1991.

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R, Dougherty Edward, and Society of Photo-optical Instrumentation Engineers., eds. Neural, morphological, and stochastic methods in image and signal processing: 10-11 July, 1995, San Diego, California. Bellingham, Wash., USA: SPIE, 1995.

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Helias, Moritz, and David Dahmen. Statistical Field Theory for Neural Networks. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46444-8.

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Części książek na temat "Neural fields"

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Coombes, Stephen, Peter beim Graben, and Roland Potthast. "Tutorial on Neural Field Theory." In Neural Fields, 1–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1_1.

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beim Graben, Peter, and Serafim Rodrigues. "On the Electrodynamics of Neural Networks." In Neural Fields, 269–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1_10.

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beim Graben, Peter, and Roland Potthast. "Universal Neural Field Computation." In Neural Fields, 299–318. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1_11.

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Lins, Jonas, and Gregor Schöner. "A Neural Approach to Cognition Based on Dynamic Field Theory." In Neural Fields, 319–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1_12.

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Erlhagen, Wolfram, and Estela Bicho. "A Dynamic Neural Field Approach to Natural and Efficient Human-Robot Collaboration." In Neural Fields, 341–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1_13.

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Liley, David T. J. "Neural Field Modelling of the Electroencephalogram: Physiological Insights and Practical Applications." In Neural Fields, 367–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1_14.

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Steyn-Ross, D. Alistair, Moira L. Steyn-Ross, and Jamie W. Sleigh. "Equilibrium and Nonequilibrium Phase Transitions in a Continuum Model of an Anesthetized Cortex." In Neural Fields, 393–416. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1_15.

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Jirsa, Viktor. "Large Scale Brain Networks of Neural Fields." In Neural Fields, 417–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1_16.

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Pinotsis, Dimitris A., and Karl J. Friston. "Neural Fields, Masses and Bayesian Modelling." In Neural Fields, 433–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1_17.

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Wright, James J., and Paul D. Bourke. "Neural Field Dynamics and the Evolution of the Cerebral Cortex." In Neural Fields, 457–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1_18.

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Streszczenia konferencji na temat "Neural fields"

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Choi, Hyunsoo, and Chulhee Lee. "Neural Network Deinterlacing Using Multiple Fields and Field-MSEs." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371072.

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Takikawa, Towaki, Alex Evans, Jonathan Tremblay, Thomas Müller, Morgan McGuire, Alec Jacobson, and Sanja Fidler. "Variable Bitrate Neural Fields." In SIGGRAPH '22: Special Interest Group on Computer Graphics and Interactive Techniques Conference. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3528233.3530727.

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Müller, Thomas, Alex Evans, Christoph Schied, Marco Foco, András Bódis-Szomorú, Isaac Deutsch, Michael Shelley, and Alexander Keller. "Instant Neural Radiance Fields." In SIGGRAPH '22: Special Interest Group on Computer Graphics and Interactive Techniques Conference. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3532833.3538678.

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Ost, Julian, Issam Laradji, Alejandro Newell, Yuval Bahat, and Felix Heide. "Neural Point Light Fields." In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.01787.

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Kim, Youngchan, Wonjoon Jin, Sunghyun Cho, and Seung-Hwan Baek. "Neural Spectro-polarimetric Fields." In SA '23: SIGGRAPH Asia 2023. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3610548.3618172.

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Tompkin, James. "Neural Fields for Scalable Scene Reconstruction." In Design Computation Input/Output 2022. Design Computation, 2022. http://dx.doi.org/10.47330/dcio.2022.axbl8798.

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Neural fields are a new (and old!) approach to solving problems over spacetime via first-order optimization of a neural network. Over the past three years, combining neural fields with classic computer graphics approaches have allowed us to make significant advances in solving computer vision problems like scene reconstruction. I will present recent work that can reconstruct indoor scenes for photorealistic interactive exploration using new scalable hybrid neural field representations. This has applications where any real-world place needs to be digitized, especially for visualization purposes
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Gu, Jeffrey, Kuan-Chieh Wang, and Serena Yeung. "Generalizable Neural Fields as Partially Observed Neural Processes." In 2023 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2023. http://dx.doi.org/10.1109/iccv51070.2023.00491.

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Luo, Haimin, Anpei Chen, Qixuan Zhang, Bai Pang, Minye Wu, Lan Xu, and Jingyi Yu. "Convolutional Neural Opacity Radiance Fields." In 2021 IEEE International Conference on Computational Photography (ICCP). IEEE, 2021. http://dx.doi.org/10.1109/iccp51581.2021.9466273.

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Kania, Kacper, Kwang Moo Yi, Marek Kowalski, Tomasz Trzciniski, and Andrea Tagliasacchi. "CoNeRF: Controllable Neural Radiance Fields." In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.01807.

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Hu, Tao, Shu Liu, Yilun Chen, Tiancheng Shen, and Jiaya Jia. "EfficientNeRF - Efficient Neural Radiance Fields." In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.01256.

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Raporty organizacyjne na temat "Neural fields"

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Burby, Joshua William, and Qi Tang. Fast neural Poincare maps for toroidal magnetic fields. Office of Scientific and Technical Information (OSTI), July 2020. http://dx.doi.org/10.2172/1637687.

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Orkwis, Paul D., and Terry Daviaux. Advanced Neural Network Modeling of Synthetic Jet Flow Fields. Fort Belvoir, VA: Defense Technical Information Center, March 2006. http://dx.doi.org/10.21236/ada473581.

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Gonzalez Pibernat, Gabriel, and Miguel Mascaró Portells. Dynamic structure of single-layer neural networks. Fundación Avanza, May 2023. http://dx.doi.org/10.60096/fundacionavanza/2392022.

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This article examines the practical applications of single hidden layer neural networks in machine learning and artificial intelligence. They have been used in diverse fields, such as finance, medicine, and autonomous vehicles, due to their simplicit
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Warrick, Arthur W., Gideon Oron, Mary M. Poulton, Rony Wallach, and Alex Furman. Multi-Dimensional Infiltration and Distribution of Water of Different Qualities and Solutes Related Through Artificial Neural Networks. United States Department of Agriculture, January 2009. http://dx.doi.org/10.32747/2009.7695865.bard.

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The project exploits the use of Artificial Neural Networks (ANN) to describe infiltration, water, and solute distribution in the soil during irrigation. It provides a method of simulating water and solute movement in the subsurface which, in principle, is different and has some advantages over the more common approach of numerical modeling of flow and transport equations. The five objectives were (i) Numerically develop a database for the prediction of water and solute distribution for irrigation; (ii) Develop predictive models using ANN; (iii) Develop an experimental (laboratory) database of
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Cooper, Leon N., and Christopher L. Scofield. Mean Field Theory of a Neural Network. Fort Belvoir, VA: Defense Technical Information Center, January 1988. http://dx.doi.org/10.21236/ada190801.

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Elliott, Daniel S., and David B. Janes. Neutral Atom Lithography With Multi-Frequency Laser Fields. Fort Belvoir, VA: Defense Technical Information Center, June 2006. http://dx.doi.org/10.21236/ada459307.

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Jau, Yuan-Yu. Imaging electric field with electrically neutral particles. Office of Scientific and Technical Information (OSTI), July 2021. http://dx.doi.org/10.2172/1821957.

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Yaroshchuk, Svitlana O., Nonna N. Shapovalova, Andrii M. Striuk, Olena H. Rybalchenko, Iryna O. Dotsenko, and Svitlana V. Bilashenko. Credit scoring model for microfinance organizations. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3683.

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The purpose of the work is the development and application of models for scoring assessment of microfinance institution borrowers. This model allows to increase the efficiency of work in the field of credit. The object of research is lending. The subject of the study is a direct scoring model for improving the quality of lending using machine learning methods. The objective of the study: to determine the criteria for choosing a solvent borrower, to develop a model for an early assessment, to create software based on neural networks to determine the probability of a loan default risk. Used rese
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Wilmont, Martyn, Greg Van Boven, and Tom Jack. GRI-96-0452_1 Stress Corrosion Cracking Under Field Simulated Conditions I. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), November 1997. http://dx.doi.org/10.55274/r0011963.

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Electrochemical measurements have been performed on polished and mill scaled steel samples. The solutions investigated have included carbonate bicarbonate mixtures of varying pH as well as solutions of neutral pH such as NS4. Results indicate that the mechanism of corrosion associated with the carbonate bicarbonate environments involves passive film formation. No such passivation is observed for solutions associated with neutral pH SCC. Electrochemical corrosion rates measured on polished steel specimens exposed to NS4 solutions in the pH range 5 to 6.8 were in the region of 5 x 10e-1 to 1 x 1
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Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, January 2022. http://dx.doi.org/10.31979/mti.2022.2014.

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As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and f
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