Literatura científica selecionada sobre o tema "Deep structures"
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Artigos de revistas sobre o assunto "Deep structures"
Nizami Huseyn, Elcin. "ELECTROSTIMULATION OF BRAIN DEEP STRUCTURES IN PARKINSON'S DISEASE". SCIENTIFIC WORK 70, n.º 09 (21 de setembro de 2021): 14–19. http://dx.doi.org/10.36719/2663-4619/70/14-19.
Texto completo da fonteSingh, Arunima. "Deep learning 3D structures". Nature Methods 17, n.º 3 (março de 2020): 249. http://dx.doi.org/10.1038/s41592-020-0779-y.
Texto completo da fonteBowles, Martin L. "Recognizing Deep Structures in Organizations". Organization Studies 11, n.º 3 (julho de 1990): 395–412. http://dx.doi.org/10.1177/017084069001100304.
Texto completo da fonteZhou, Ding-Xuan. "Deep distributed convolutional neural networks: Universality". Analysis and Applications 16, n.º 06 (novembro de 2018): 895–919. http://dx.doi.org/10.1142/s0219530518500124.
Texto completo da fontePodoski, Jessica H., Thomas D. Smith, David C. Finnegan, Adam L. LeWinter e Peter J. Gadomski. "UNMANNED AERIAL SYSTEM LIDAR SURVEY OF TWO BREAKWATERS IN THE HAWAIIAN ISLANDS". Coastal Engineering Proceedings, n.º 36 (30 de dezembro de 2018): 23. http://dx.doi.org/10.9753/icce.v36.structures.23.
Texto completo da fonteHao, Xing, Guigang Zhang e Shang Ma. "Deep Learning". International Journal of Semantic Computing 10, n.º 03 (setembro de 2016): 417–39. http://dx.doi.org/10.1142/s1793351x16500045.
Texto completo da fonteEliava, Shalva, Oleg Shekhtman e Mariya Varyukhina. "Microsurgical Angioarchitectonics of Deep Brain Structures and Deep Arterial Anastomoses". World Neurosurgery 126 (junho de 2019): e1092-e1098. http://dx.doi.org/10.1016/j.wneu.2019.02.213.
Texto completo da fonteGooderham, David. "Deep calling unto deep: Pre-oedipal structures in children's texts". Childrens Literature in Education 25, n.º 2 (junho de 1994): 113–23. http://dx.doi.org/10.1007/bf02355399.
Texto completo da fonteKalygina, V. M., Yu S. Petrova, I. A. Prudaev, O. P. Tolbanov e S. Yu Tsupiy. "Deep centers in TiO2-Si structures". Semiconductors 49, n.º 8 (agosto de 2015): 1012–18. http://dx.doi.org/10.1134/s1063782615080102.
Texto completo da fonteKasztelanic, Rafał. "Multilevel structures in deep proton lithography". Journal of Micro/Nanolithography, MEMS, and MOEMS 7, n.º 1 (1 de janeiro de 2008): 013006. http://dx.doi.org/10.1117/1.2841721.
Texto completo da fonteTeses / dissertações sobre o assunto "Deep structures"
Lambert, C. P. "Multimodal segmentation of deep cortical structures". Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1344055/.
Texto completo da fonteXu, Yuan. "Statistical shape analysis for deep brain structures". Diss., Restricted to subscribing institutions, 2008. http://proquest.umi.com/pqdweb?did=1581917061&sid=11&Fmt=2&clientId=1564&RQT=309&VName=PQD.
Texto completo da fonteBillingsley, Richard John. "Deep Learning for Semantic and Syntactic Structures". Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/12825.
Texto completo da fonteOlowe, Adedayo Christianah. "Corrosion assessment and cathodic protection design parameters for steel structures in deep and ultra deep offshore waters". Thesis, University of Aberdeen, 2013. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=201965.
Texto completo da fonteGrice, James Robert. "Prediction of extreme wave-structure interactions for multi-columned structures in deep water". Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:dd7320c1-7121-4ea7-827f-527af9405e9a.
Texto completo da fonteDikdogmus, Halil. "RISER CONCEPTS FOR DEEP WATERS". Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for marin teknikk, 2012. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-18528.
Texto completo da fonteRomagna, Pinter Patricia. "Reappraising the Numidian system (Miocene, southern Italy) deep-water sandstone fairways confined by tectonised substrate". Thesis, University of Aberdeen, 2017. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=238534.
Texto completo da fonteOyallon, Edouard. "Analyzing and introducing structures in deep convolutional neural networks". Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE060.
Texto completo da fonteThis thesis studies empirical properties of deep convolutional neural networks, and in particular the Scattering Transform. Indeed, the theoretical analysis of the latter is hard and until now remains a challenge: successive layers of neurons have the ability to produce complex computations, whose nature is still unknown, thanks to learning algorithms whose convergence guarantees are not well understood. However, those neural networks are outstanding tools to tackle a wide variety of difficult tasks, like image classification or more formally statistical prediction. The Scattering Transform is a non-linear mathematical operator whose properties are inspired by convolutional networks. In this work, we apply it to natural images, and obtain competitive accuracies with unsupervised architectures. Cascading a supervised neural networks after the Scattering permits to compete on ImageNet2012, which is the largest dataset of labeled images available. An efficient GPU implementation is provided. Then, this thesis focuses on the properties of layers of neurons at various depths. We show that a progressive dimensionality reduction occurs and we study the numerical properties of the supervised classification when we vary the hyper parameters of the network. Finally, we introduce a new class of convolutional networks, whose linear operators are structured by the symmetry groups of the classification task
Astolfi, Pietro. "Toward the "Deep Learning" of Brain White Matter Structures". Doctoral thesis, Università degli studi di Trento, 2022. http://hdl.handle.net/11572/337629.
Texto completo da fonteYang, Yuzhe S. M. Massachusetts Institute of Technology. "On exploiting structures for deep learning algorithms with matrix estimation". Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/127319.
Texto completo da fonteCataloged from the official PDF of thesis.
Includes bibliographical references (pages 113-118).
Despite recent breakthroughs of deep learning, the intrinsic structures within tasks have not yet been fully explored and exploited for better performance. This thesis proposes to harness the structured properties of deep learning tasks using matrix estimation (ME). Motivated by the theoretical guarantees and appealing results, we apply ME to study the following two important learning problems: 1. Adversarial robustness. Deep neural networks are vulnerable to adversarial attacks. This thesis proposes ME-Net, a defense method that leverages ME. In ME-Net, images are preprocessed using two steps: first pixels are randomly dropped from the image; then, the image is reconstructed using ME. We show that this process destroys the adversarial structure of the noise, while re-enforcing the global structure in the original image. Comparing ME-Net with state-of-the-art defense mechanisms shows that ME-Net consistently outperforms prior techniques, improving robustness against both black-box and white-box attacks. 2. Value-based planning and deep reinforcement learning (RL). This thesis proposes to exploit the underlying low-rank structures of the state-action value function, i.e., Q function. We verify empirically the existence of low-rank Q functions in the context of control and deep RL tasks. As our key contribution, by leveraging ME, we propose a generic framework to exploit the underlying low-rank structure in Q functions. This leads to a more efficient planning procedure for classical control, and additionally, a simple scheme that can be applied to any value-based RL techniques to consistently achieve better performance on "low-rank" tasks. The results of this thesis demonstrate the value of using matrix estimation to capture the internal structures of deep learning tasks, and highlight the benefits of leveraging structure for analyzing and improving modern learning algorithms.
by Yuzhe Yang.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Livros sobre o assunto "Deep structures"
Petersen, Ib Damgaard. Deep structures in international politics. København, Danmark: Institute of Political Studies, University of Copenhagen, 1987.
Encontre o texto completo da fonteLucerna, Sebastiano, Francesco M. Salpietro, Concetta Alafaci e Francesco Tomasello. In Vivo Atlas of Deep Brain Structures. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56381-2.
Texto completo da fontePinto, Pedro, Chang-Yu Ou e Hany Shehata, eds. Innovative Solutions for Deep Foundations and Retaining Structures. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34190-9.
Texto completo da fonteMaccarini, Andrea M. Deep Change and Emergent Structures in Global Society. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13624-6.
Texto completo da fonte1957-, Lucerna S., ed. In vivo atlas of deep brain structures: With 3D reconstructions. Berlin: Springer, 2002.
Encontre o texto completo da fonteEngineers, Institution of Structural. Design and construction of deep basements including cut-and-cover structures. London: The Institution, 2004.
Encontre o texto completo da fonteEngineers, Institution of Structural, ed. Design and construction of deep basements including cut-and-cover structures. London: Institution of Structural Engineers, 2004.
Encontre o texto completo da fonteInc, BarCharts, ed. Anatomy 2: Includes deep and posterior anatomy plus many new structures. [Boca Raton, Fla.]: BarCharts, Inc., 2005.
Encontre o texto completo da fonteAndrew St. Lawrence John Wickens. The Trinity and anthropology: The philosophical deep structures of Karl Rahner's theology. Birmingham: University of Birmingham, 1994.
Encontre o texto completo da fonte1943-, Stecker Michael, ed. Structures in space: Hidden secrets of the deep sky : the Stecker files. New York: Springer, 2000.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Deep structures"
Strenski, Ivan. "Falsifying Deep Structures". In Religion in Relation, 57–74. London: Palgrave Macmillan UK, 1993. http://dx.doi.org/10.1007/978-1-349-11866-3_4.
Texto completo da fonteCaracas, Razvan. "Crystal Structures of Core Materials". In Deep Earth, 55–68. Hoboken, NJ: John Wiley & Sons, Inc, 2016. http://dx.doi.org/10.1002/9781118992487.ch5.
Texto completo da fonteOsipyan, Hasmik, Bosede Iyiade Edwards e Adrian David Cheok. "Neural Network Structures". In Deep Neural Network Applications, 29–55. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9780429265686-3.
Texto completo da fonteWicks, June K., e Thomas S. Duffy. "Crystal Structures of Minerals in the Lower Mantle". In Deep Earth, 69–87. Hoboken, NJ: John Wiley & Sons, Inc, 2016. http://dx.doi.org/10.1002/9781118992487.ch6.
Texto completo da fonteBen-Menahem, Ari. "Deep Principles – Complex Structures". In Historical Encyclopedia of Natural and Mathematical Sciences, 5081–986. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-68832-7_9.
Texto completo da fonteEppelbaum, Lev, Izzy Kutasov e Arkady Pilchin. "Investigating Deep Lithospheric Structures". In Lecture Notes in Earth System Sciences, 269–391. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-34023-9_6.
Texto completo da fonteMcCawley, James D. "On what is Deep about Deep Structures". In Cognition and the Symbolic Processes, 125–28. London: Routledge, 2024. http://dx.doi.org/10.4324/9781003482833-5.
Texto completo da fonteMirtskhulava, Lela. "Deep Learning Applications in Predicting Polymer Properties". In Advanced Polymer Structures, 161–71. New York: Apple Academic Press, 2023. http://dx.doi.org/10.1201/9781003352181-16.
Texto completo da fonteJishun, Ren, Jiang Chunfa, Zhang Zhengkun e Qin Deyu. "Deep Fractures and Deep-Seated Structures in China". In Geotectonic Evolution of China, 126–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 1987. http://dx.doi.org/10.1007/978-3-642-61574-0_5.
Texto completo da fonteBédaride, Paul, e Claire Gardent. "Deep Semantics for Dependency Structures". In Computational Linguistics and Intelligent Text Processing, 277–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19400-9_22.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Deep structures"
Chikersal, Prerna, Maria Tomprou, Young Ji Kim, Anita Williams Woolley e Laura Dabbish. "Deep Structures of Collaboration". In CSCW '17: Computer Supported Cooperative Work and Social Computing. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/2998181.2998250.
Texto completo da fonteGAWRONSKI, W., B. BIENKIEWICZ e R. HILL. "Wind-induced dynamics of the deep space network antennas". In 33rd Structures, Structural Dynamics and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1992. http://dx.doi.org/10.2514/6.1992-2458.
Texto completo da fonteScalise, Carmen, e Kevin Fitzpatrick. "Chicago Deep Tunnel Design and Construction". In Structures Congress 2012. Reston, VA: American Society of Civil Engineers, 2012. http://dx.doi.org/10.1061/9780784412367.132.
Texto completo da fonteHan, Jie, e Ken Akins. "Use of Geogrid-Reinforced and Pile-Supported Earth Structures". In International Deep Foundations Congress 2002. Reston, VA: American Society of Civil Engineers, 2002. http://dx.doi.org/10.1061/40601(256)48.
Texto completo da fonteBirrcher, David, Robin Tuchscherer, Matthew Huizinga e Oguzhan Bayrak. "Depth Effect in Reinforced Concrete Deep Beams". In Structures Congress 2009. Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41031(341)175.
Texto completo da fonteBi, Zhnegfa, e Xinming Wu. "Implicit structural modeling of geological structures with deep learning". In First International Meeting for Applied Geoscience & Energy. Society of Exploration Geophysicists, 2021. http://dx.doi.org/10.1190/segam2021-3583427.1.
Texto completo da fontePhoon, Kok-Kwang, e Fred H. Kulhawy. "Drilled Shaft Design for Transmission Structures Using LRFD and MRFD". In International Deep Foundations Congress 2002. Reston, VA: American Society of Civil Engineers, 2002. http://dx.doi.org/10.1061/40601(256)70.
Texto completo da fonteWu, Xiong-Jian, e W. G. Price. "The Behaviour of Shallow Draft Offshore Structures and Service Vessels in Deeper Water". In Development In Deep Waters. RINA, 1986. http://dx.doi.org/10.3940/rina.ddw.1986.17.
Texto completo da fonteBouadi, Hakim, Eric Green e Narendra Gosain. "Evaluation and Repair of a Deep Transfer Girder". In Structures Congress 2005. Reston, VA: American Society of Civil Engineers, 2005. http://dx.doi.org/10.1061/40753(171)255.
Texto completo da fonteTomaszkiewicz, Karolina, e Tomasz Owerko. "Deep machine learning in bridge structures durability analysis". In 5th Joint International Symposium on Deformation Monitoring. Valencia: Editorial de la Universitat Politècnica de València, 2022. http://dx.doi.org/10.4995/jisdm2022.2022.13884.
Texto completo da fonteRelatórios de organizações sobre o assunto "Deep structures"
Harris, L. B., P. Adiban e E. Gloaguen. The role of enigmatic deep crustal and upper mantle structures on Au and magmatic Ni-Cu-PGE-Cr mineralization in the Superior Province. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328984.
Texto completo da fonteClay, C. S. Acoustic Reverberation in Wedge Structures at the Transitions from Deep to Shallow Water. Fort Belvoir, VA: Defense Technical Information Center, agosto de 1997. http://dx.doi.org/10.21236/ada328801.
Texto completo da fonteVIGIL, MANUEL GILBERT. Design of Largest Shaped Charge: Generation of Very Large Diameter, Deep Holes in Rock and Concrete Structures. Office of Scientific and Technical Information (OSTI), abril de 2003. http://dx.doi.org/10.2172/810682.
Texto completo da fonteBernau, Jeremiah A., Charles G. Oviatt, Donald L. Clark e Brenda B. Bowen. Sediment Logs Compiled From the Great Salt Lake Desert, Western Utah, With a Focus on the Bonneville Salt Flats Area. Utah Geological Survey, junho de 2023. http://dx.doi.org/10.34191/ofr-754.
Texto completo da fonteVito, L. F. Di, G. Mannucci, G. Demofonti, G. Cumino, A. Izquierdo, F. Daguerre, H. Quintanille e M. Tivelli. CGX-00-003 Tenaris Double Joint for Deep Water Applications Subjected to Large Cyclic Plastic Strains. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), agosto de 1994. http://dx.doi.org/10.55274/r0011808.
Texto completo da fonteBourhrous, Amal, Shivan Fazil e Dylan O’Driscoll. Post-conflict Reconstruction in the Nineveh Plains of Iraq: Agriculture, Cultural Practices and Social Cohesion. Stockholm International Peace Research Institute, novembro de 2022. http://dx.doi.org/10.55163/raep9560.
Texto completo da fonteNg, Andrew Y., e Christopher D. Manning. Discovery of Deep Structure from Unlabeled Data. Fort Belvoir, VA: Defense Technical Information Center, novembro de 2014. http://dx.doi.org/10.21236/ada614158.
Texto completo da fonteHeaney, Kevin. Spatial Structure of Deep Water Acoustic Propagation. Fort Belvoir, VA: Defense Technical Information Center, setembro de 2008. http://dx.doi.org/10.21236/ada533364.
Texto completo da fonteDafoe, L. T., K. Dickie e G. L. Williams. Stratigraphy of western Baffin Bay: a review of existing knowledge and some new insights. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/321846.
Texto completo da fonteCopeland, Ronald, e James Lewis. Technical assessment of the Old, Mississippi, Atchafalaya, and Red (OMAR) Rivers: Mississippi River HEC-6T model. Engineer Research and Development Center (U.S.), agosto de 2022. http://dx.doi.org/10.21079/11681/45160.
Texto completo da fonte