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Artykuły w czasopismach na temat "Neural fields"
Coombes, Stephen. "Neural fields". Scholarpedia 1, nr 6 (2006): 1373. http://dx.doi.org/10.4249/scholarpedia.1373.
Pełny tekst źródłaAigerman, Noam, Kunal Gupta, Vladimir G. Kim, Siddhartha Chaudhuri, Jun Saito i Thibault Groueix. "Neural jacobian fields". ACM Transactions on Graphics 41, nr 4 (lipiec 2022): 1–17. http://dx.doi.org/10.1145/3528223.3530141.
Pełny tekst źródłaSmaragdis, Paris. "Neural acoustic fields". Journal of the Acoustical Society of America 153, nr 3_supplement (1.03.2023): A175. http://dx.doi.org/10.1121/10.0018569.
Pełny tekst źródłaFriston, Karl. "Mean-Fields and Neural Masses". PLoS Computational Biology 4, nr 8 (29.08.2008): e1000081. http://dx.doi.org/10.1371/journal.pcbi.1000081.
Pełny tekst źródłaChappet De Vangel, Benoît, Cesar Torres-huitzil i Bernard Girau. "Randomly Spiking Dynamic Neural Fields". ACM Journal on Emerging Technologies in Computing Systems 11, nr 4 (27.04.2015): 1–26. http://dx.doi.org/10.1145/2629517.
Pełny tekst źródłaIgel, Christian, Wolfram Erlhagen i Dirk Jancke. "Optimization of dynamic neural fields". Neurocomputing 36, nr 1-4 (luty 2001): 225–33. http://dx.doi.org/10.1016/s0925-2312(00)00328-3.
Pełny tekst źródłaBelhe, Yash, Michaël Gharbi, Matthew Fisher, Iliyan Georgiev, Ravi Ramamoorthi i Tzu-Mao Li. "Discontinuity-Aware 2D Neural Fields". ACM Transactions on Graphics 42, nr 6 (5.12.2023): 1–11. http://dx.doi.org/10.1145/3618379.
Pełny tekst źródłaEsselle, K. P., i M. A. Stuchly. "Neural stimulation with magnetic fields: analysis of induced electric fields". IEEE Transactions on Biomedical Engineering 39, nr 7 (lipiec 1992): 693–700. http://dx.doi.org/10.1109/10.142644.
Pełny tekst źródłaBressloff, Paul C., i Matthew A. Webber. "Front Propagation in Stochastic Neural Fields". SIAM Journal on Applied Dynamical Systems 11, nr 2 (styczeń 2012): 708–40. http://dx.doi.org/10.1137/110851031.
Pełny tekst źródłaKilpatrick, Zachary P., i Grégory Faye. "Pulse Bifurcations in Stochastic Neural Fields". SIAM Journal on Applied Dynamical Systems 13, nr 2 (styczeń 2014): 830–60. http://dx.doi.org/10.1137/140951369.
Pełny tekst źródłaRozprawy doktorskie na temat "Neural fields"
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.
Pełny tekst źródła京都大学
新制・課程博士
博士(工学)
甲第23205号
工博第4849号
京都大学大学院工学研究科電気工学専攻
(主査)教授 小林 哲生, 教授 松尾 哲司, 特定教授 中村 武恒
学位規則第4条第1項該当
Doctor of Philosophy (Engineering)
Kyoto University
DFAM
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.
Pełny tekst źródłaDavenport, 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.
Pełny tekst źródłaArocena, 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.
Pełny tekst źródłaFerguson, 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.
Pełny tekst źródłaQi, Yang. "Anomalous neural pattern dynamics: formation mechanisms and functional roles". Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/18808.
Pełny tekst źródłaRohlén, 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.
Pełny tekst źródłaObemannade autonoma luftburna fordons (UAV) förmåga att lokaliera sig själva är fundamental för att de ska fungera, även om de inte har tillgång till globala positioneringssystem. Med den nyliga framgången hos djupinlärning applicerat på visuella problem har det kommit metoder för absolut geolokalisering med visuell djupinlärning med satellit- och UAV-bilder. De flesta av dessa metoder har bara blivit testade i stadsmiljöer, vilket leder till frågan: Hur väl fungerar dessa metoder i icke-urbana områden som fält och skogar? En av nackdelarna med djupinlärning är att dessa modeller ofta ses som svarta lådor eftersom det är svårt att veta varför modellerna gör de gissningar de gör, alltså vilken information som är viktig och används för gissningen. För att lösa detta har flera metoder för att tolka neurala nätverk utvecklats. Dessa metoder ger förklaringar så att vi kan förstå dessa modeller bättre. Denna uppsats undersöker lokaliseringsprecisionen hos en geolokaliseringsmetod i både urbana och icke-urbana miljöer och applicerar även en tolkningsmetod för neurala nätverk för att se ifall den kan förklara den potentialla skillnaden i precision hos metoden i dessa olika miljöer. Resultaten visar att metoden fungerar bäst i urbana miljöer där den får ett genomsnittligt absolut horisontellt lokaliseringsfel på 38.30m och ett genomsnittligt absolut vertikalt fel på 16.77m medan den presterade signifikant sämre i icke-urbana miljöer där den fick ett genomsnittligt absolut horisontellt lokaliseringsfel på 68.11m och ett genomsnittligt absolut vertikalt fel på 22.83m. Vidare visar resultaten att om satellitbilderna och UAV-bilderna är tagna från olika årstider blir lokaliseringsprecisionen ännu sämre, där metoden får genomsnittligt absolut horisontellt lokaliseringsfel på 86.91m och ett genomsnittligt absolut vertikalt fel på 23.05m. Tolkningsmetoden hjälpte inte i att förklara varför metoden fungerar sämre i icke-urbana miljöer och är inte passande att använda för denna sortens problem.
Curtis, Maurice A. "Neural progenitor cells in the Huntington's Disease human brain". Thesis, University of Auckland, 2004. http://hdl.handle.net/2292/3114.
Pełny tekst źródłaZhang, 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.
Pełny tekst źródłaHarris, 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.
Pełny tekst źródłaCataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 22-24).
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 accuracy, their computational complexity limits applications to small systems and short timescales. In practice, the PES must be approximated using a computationally cheaper functional form. Classical force field (CFF) approaches simply define the PES as a sum over independent energy contributions. Commonly included terms include bonded (pair, angle, dihedral, etc.) and non bonded (van der Waals, Coulomb, etc.) interactions, while more recent CFFs model polarizability, reactivity, and other higher-order interactions.
Simple, physically-justified functional forms are often implemented for each energy type, but this choice - and the choice of which energy terms to include in the first place - is arbitrary and often hand-tuned on a per-system basis, severely limiting PES transferability. This flexibility has complicated the quest for a universal CFF. The simplest usable CFFs are tailored to specific classes of molecules and have few parameters, so that they can be optimally parameterized using a small amount of data; however, they suffer low transferability. Highly-parameterized neural network potentials can yield predictions that are extremely accurate for the entire training set; however, they suffer over-fitting and cannot interpolate.
We develop a tool, called AuTopology, to explore the trade-offs between complexity and generalizability in fitting CFFs; focus on simple, computationally fast functions that enforce physics-based regularization and transferability; use message-passing neural networks to featurized molecular graphs and interpolate CFF parameters across chemical space; and utilize high performance computing resources to improve the efficiency of model training and usage. A universal, fast CFF would open the door to high-throughput virtual materials screening in the pursuit of novel materials with tailored properties.
by William H. Harris.
S.M.
S.M. Massachusetts Institute of Technology, Department of Materials Science and Engineering
Książki na temat "Neural fields"
Coombes, Stephen, Peter beim Graben, Roland Potthast i James Wright, red. Neural Fields. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1.
Pełny tekst źródła1919-, Pribram Karl H., i Eccles, John C. Sir, 1903-, red. Rethinking neural networks: Quantum fields and biological data. Hillsdale, N.J: Erlbaum, 1993.
Znajdź pełny tekst źródłaB, Pinter Robert, i Nabet Bahram, red. Nonlinear vision: Determination of neural receptive fields, function, and networks. Boca Raton: CRC Press, 1992.
Znajdź pełny tekst źródłaKozma, Robert, i 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.
Pełny tekst źródłaHorowitz, John. The effects of hypergravic fields on neural signalling in the hippocampus. [Washington, DC: National Aeronautics and Space Administration, 1991.
Znajdź pełny tekst źródłaBooth, John Nicholas. The application of weak complex magnetic fields on the neural correlates of consciousness. Sudbury, Ont: Laurentian University, School of Graduate Studies, 2006.
Znajdź pełny tekst źródła1919-, Pribram Karl H., i Eccles, John C. Sir, 1903-, red. Rethinking neural networks: Quantum fields and biological data : proceedings of the First Appalachian Conference on Behavioral Neurodynamics. Hillsdale, N.J: Erlbaum, 1993.
Znajdź pełny tekst źródłaCenter, Ames Research, red. 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.
Znajdź pełny tekst źródłaR, Dougherty Edward, i Society of Photo-optical Instrumentation Engineers., red. Neural, morphological, and stochastic methods in image and signal processing: 10-11 July, 1995, San Diego, California. Bellingham, Wash., USA: SPIE, 1995.
Znajdź pełny tekst źródłaHelias, Moritz, i David Dahmen. Statistical Field Theory for Neural Networks. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46444-8.
Pełny tekst źródłaCzęści książek na temat "Neural fields"
Coombes, Stephen, Peter beim Graben i Roland Potthast. "Tutorial on Neural Field Theory". W Neural Fields, 1–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1_1.
Pełny tekst źródłabeim Graben, Peter, i Serafim Rodrigues. "On the Electrodynamics of Neural Networks". W Neural Fields, 269–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1_10.
Pełny tekst źródłabeim Graben, Peter, i Roland Potthast. "Universal Neural Field Computation". W Neural Fields, 299–318. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1_11.
Pełny tekst źródłaLins, Jonas, i Gregor Schöner. "A Neural Approach to Cognition Based on Dynamic Field Theory". W Neural Fields, 319–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1_12.
Pełny tekst źródłaErlhagen, Wolfram, i Estela Bicho. "A Dynamic Neural Field Approach to Natural and Efficient Human-Robot Collaboration". W Neural Fields, 341–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1_13.
Pełny tekst źródłaLiley, David T. J. "Neural Field Modelling of the Electroencephalogram: Physiological Insights and Practical Applications". W Neural Fields, 367–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1_14.
Pełny tekst źródłaSteyn-Ross, D. Alistair, Moira L. Steyn-Ross i Jamie W. Sleigh. "Equilibrium and Nonequilibrium Phase Transitions in a Continuum Model of an Anesthetized Cortex". W Neural Fields, 393–416. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1_15.
Pełny tekst źródłaJirsa, Viktor. "Large Scale Brain Networks of Neural Fields". W Neural Fields, 417–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1_16.
Pełny tekst źródłaPinotsis, Dimitris A., i Karl J. Friston. "Neural Fields, Masses and Bayesian Modelling". W Neural Fields, 433–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1_17.
Pełny tekst źródłaWright, James J., i Paul D. Bourke. "Neural Field Dynamics and the Evolution of the Cerebral Cortex". W Neural Fields, 457–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-54593-1_18.
Pełny tekst źródłaStreszczenia konferencji na temat "Neural fields"
Choi, Hyunsoo, i Chulhee Lee. "Neural Network Deinterlacing Using Multiple Fields and Field-MSEs". W 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371072.
Pełny tekst źródłaTakikawa, Towaki, Alex Evans, Jonathan Tremblay, Thomas Müller, Morgan McGuire, Alec Jacobson i Sanja Fidler. "Variable Bitrate Neural Fields". W 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.
Pełny tekst źródłaMüller, Thomas, Alex Evans, Christoph Schied, Marco Foco, András Bódis-Szomorú, Isaac Deutsch, Michael Shelley i Alexander Keller. "Instant Neural Radiance Fields". W 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.
Pełny tekst źródłaOst, Julian, Issam Laradji, Alejandro Newell, Yuval Bahat i Felix Heide. "Neural Point Light Fields". W 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.01787.
Pełny tekst źródłaKim, Youngchan, Wonjoon Jin, Sunghyun Cho i Seung-Hwan Baek. "Neural Spectro-polarimetric Fields". W SA '23: SIGGRAPH Asia 2023. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3610548.3618172.
Pełny tekst źródłaTompkin, James. "Neural Fields for Scalable Scene Reconstruction". W Design Computation Input/Output 2022. Design Computation, 2022. http://dx.doi.org/10.47330/dcio.2022.axbl8798.
Pełny tekst źródłaGu, Jeffrey, Kuan-Chieh Wang i Serena Yeung. "Generalizable Neural Fields as Partially Observed Neural Processes". W 2023 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2023. http://dx.doi.org/10.1109/iccv51070.2023.00491.
Pełny tekst źródłaLuo, Haimin, Anpei Chen, Qixuan Zhang, Bai Pang, Minye Wu, Lan Xu i Jingyi Yu. "Convolutional Neural Opacity Radiance Fields". W 2021 IEEE International Conference on Computational Photography (ICCP). IEEE, 2021. http://dx.doi.org/10.1109/iccp51581.2021.9466273.
Pełny tekst źródłaKania, Kacper, Kwang Moo Yi, Marek Kowalski, Tomasz Trzciniski i Andrea Tagliasacchi. "CoNeRF: Controllable Neural Radiance Fields". W 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.01807.
Pełny tekst źródłaHu, Tao, Shu Liu, Yilun Chen, Tiancheng Shen i Jiaya Jia. "EfficientNeRF - Efficient Neural Radiance Fields". W 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2022. http://dx.doi.org/10.1109/cvpr52688.2022.01256.
Pełny tekst źródłaRaporty organizacyjne na temat "Neural fields"
Burby, Joshua William, i Qi Tang. Fast neural Poincare maps for toroidal magnetic fields. Office of Scientific and Technical Information (OSTI), lipiec 2020. http://dx.doi.org/10.2172/1637687.
Pełny tekst źródłaOrkwis, Paul D., i Terry Daviaux. Advanced Neural Network Modeling of Synthetic Jet Flow Fields. Fort Belvoir, VA: Defense Technical Information Center, marzec 2006. http://dx.doi.org/10.21236/ada473581.
Pełny tekst źródłaGonzalez Pibernat, Gabriel, i Miguel Mascaró Portells. Dynamic structure of single-layer neural networks. Fundación Avanza, maj 2023. http://dx.doi.org/10.60096/fundacionavanza/2392022.
Pełny tekst źródłaWarrick, Arthur W., Gideon Oron, Mary M. Poulton, Rony Wallach i Alex Furman. Multi-Dimensional Infiltration and Distribution of Water of Different Qualities and Solutes Related Through Artificial Neural Networks. United States Department of Agriculture, styczeń 2009. http://dx.doi.org/10.32747/2009.7695865.bard.
Pełny tekst źródłaCooper, Leon N., i Christopher L. Scofield. Mean Field Theory of a Neural Network. Fort Belvoir, VA: Defense Technical Information Center, styczeń 1988. http://dx.doi.org/10.21236/ada190801.
Pełny tekst źródłaElliott, Daniel S., i David B. Janes. Neutral Atom Lithography With Multi-Frequency Laser Fields. Fort Belvoir, VA: Defense Technical Information Center, czerwiec 2006. http://dx.doi.org/10.21236/ada459307.
Pełny tekst źródłaJau, Yuan-Yu. Imaging electric field with electrically neutral particles. Office of Scientific and Technical Information (OSTI), lipiec 2021. http://dx.doi.org/10.2172/1821957.
Pełny tekst źródłaYaroshchuk, Svitlana O., Nonna N. Shapovalova, Andrii M. Striuk, Olena H. Rybalchenko, Iryna O. Dotsenko i Svitlana V. Bilashenko. Credit scoring model for microfinance organizations. [б. в.], luty 2020. http://dx.doi.org/10.31812/123456789/3683.
Pełny tekst źródłaWilmont, Martyn, Greg Van Boven i Tom Jack. GRI-96-0452_1 Stress Corrosion Cracking Under Field Simulated Conditions I. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), listopad 1997. http://dx.doi.org/10.55274/r0011963.
Pełny tekst źródłaTayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, styczeń 2022. http://dx.doi.org/10.31979/mti.2022.2014.
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