Literatura académica sobre el tema "Deep structures"
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Artículos de revistas sobre el tema "Deep structures"
Nizami Huseyn, Elcin. "ELECTROSTIMULATION OF BRAIN DEEP STRUCTURES IN PARKINSON'S DISEASE". SCIENTIFIC WORK 70, n.º 09 (21 de septiembre de 2021): 14–19. http://dx.doi.org/10.36719/2663-4619/70/14-19.
Texto completoSingh, Arunima. "Deep learning 3D structures". Nature Methods 17, n.º 3 (marzo de 2020): 249. http://dx.doi.org/10.1038/s41592-020-0779-y.
Texto completoBowles, Martin L. "Recognizing Deep Structures in Organizations". Organization Studies 11, n.º 3 (julio de 1990): 395–412. http://dx.doi.org/10.1177/017084069001100304.
Texto completoZhou, Ding-Xuan. "Deep distributed convolutional neural networks: Universality". Analysis and Applications 16, n.º 06 (noviembre de 2018): 895–919. http://dx.doi.org/10.1142/s0219530518500124.
Texto completoPodoski, Jessica H., Thomas D. Smith, David C. Finnegan, Adam L. LeWinter y Peter J. Gadomski. "UNMANNED AERIAL SYSTEM LIDAR SURVEY OF TWO BREAKWATERS IN THE HAWAIIAN ISLANDS". Coastal Engineering Proceedings, n.º 36 (30 de diciembre de 2018): 23. http://dx.doi.org/10.9753/icce.v36.structures.23.
Texto completoHao, Xing, Guigang Zhang y Shang Ma. "Deep Learning". International Journal of Semantic Computing 10, n.º 03 (septiembre de 2016): 417–39. http://dx.doi.org/10.1142/s1793351x16500045.
Texto completoEliava, Shalva, Oleg Shekhtman y Mariya Varyukhina. "Microsurgical Angioarchitectonics of Deep Brain Structures and Deep Arterial Anastomoses". World Neurosurgery 126 (junio de 2019): e1092-e1098. http://dx.doi.org/10.1016/j.wneu.2019.02.213.
Texto completoGooderham, David. "Deep calling unto deep: Pre-oedipal structures in children's texts". Childrens Literature in Education 25, n.º 2 (junio de 1994): 113–23. http://dx.doi.org/10.1007/bf02355399.
Texto completoKalygina, V. M., Yu S. Petrova, I. A. Prudaev, O. P. Tolbanov y 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 completoKasztelanic, Rafał. "Multilevel structures in deep proton lithography". Journal of Micro/Nanolithography, MEMS, and MOEMS 7, n.º 1 (1 de enero de 2008): 013006. http://dx.doi.org/10.1117/1.2841721.
Texto completoTesis sobre el tema "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 completoXu, 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 completoBillingsley, Richard John. "Deep Learning for Semantic and Syntactic Structures". Thesis, The University of Sydney, 2014. http://hdl.handle.net/2123/12825.
Texto completoOlowe, 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 completoGrice, 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 completoDikdogmus, 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 completoRomagna, 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 completoOyallon, Edouard. "Analyzing and introducing structures in deep convolutional neural networks". Thesis, Paris Sciences et Lettres (ComUE), 2017. http://www.theses.fr/2017PSLEE060.
Texto completoThis 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 completoYang, 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 completoCataloged 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
Libros sobre el tema "Deep structures"
Petersen, Ib Damgaard. Deep structures in international politics. København, Danmark: Institute of Political Studies, University of Copenhagen, 1987.
Buscar texto completoLucerna, Sebastiano, Francesco M. Salpietro, Concetta Alafaci y 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 completoPinto, Pedro, Chang-Yu Ou y 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 completoMaccarini, 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 completo1957-, Lucerna S., ed. In vivo atlas of deep brain structures: With 3D reconstructions. Berlin: Springer, 2002.
Buscar texto completoEngineers, Institution of Structural. Design and construction of deep basements including cut-and-cover structures. London: The Institution, 2004.
Buscar texto completoEngineers, Institution of Structural, ed. Design and construction of deep basements including cut-and-cover structures. London: Institution of Structural Engineers, 2004.
Buscar texto completoInc, BarCharts, ed. Anatomy 2: Includes deep and posterior anatomy plus many new structures. [Boca Raton, Fla.]: BarCharts, Inc., 2005.
Buscar texto completoAndrew St. Lawrence John Wickens. The Trinity and anthropology: The philosophical deep structures of Karl Rahner's theology. Birmingham: University of Birmingham, 1994.
Buscar texto completo1943-, Stecker Michael, ed. Structures in space: Hidden secrets of the deep sky : the Stecker files. New York: Springer, 2000.
Buscar texto completoCapítulos de libros sobre el tema "Deep structures"
Strenski, Ivan. "Falsifying Deep Structures". En Religion in Relation, 57–74. London: Palgrave Macmillan UK, 1993. http://dx.doi.org/10.1007/978-1-349-11866-3_4.
Texto completoCaracas, Razvan. "Crystal Structures of Core Materials". En Deep Earth, 55–68. Hoboken, NJ: John Wiley & Sons, Inc, 2016. http://dx.doi.org/10.1002/9781118992487.ch5.
Texto completoOsipyan, Hasmik, Bosede Iyiade Edwards y Adrian David Cheok. "Neural Network Structures". En Deep Neural Network Applications, 29–55. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9780429265686-3.
Texto completoWicks, June K. y Thomas S. Duffy. "Crystal Structures of Minerals in the Lower Mantle". En Deep Earth, 69–87. Hoboken, NJ: John Wiley & Sons, Inc, 2016. http://dx.doi.org/10.1002/9781118992487.ch6.
Texto completoBen-Menahem, Ari. "Deep Principles – Complex Structures". En 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 completoEppelbaum, Lev, Izzy Kutasov y Arkady Pilchin. "Investigating Deep Lithospheric Structures". En 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 completoMcCawley, James D. "On what is Deep about Deep Structures". En Cognition and the Symbolic Processes, 125–28. London: Routledge, 2024. http://dx.doi.org/10.4324/9781003482833-5.
Texto completoMirtskhulava, Lela. "Deep Learning Applications in Predicting Polymer Properties". En Advanced Polymer Structures, 161–71. New York: Apple Academic Press, 2023. http://dx.doi.org/10.1201/9781003352181-16.
Texto completoJishun, Ren, Jiang Chunfa, Zhang Zhengkun y Qin Deyu. "Deep Fractures and Deep-Seated Structures in China". En 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 completoBédaride, Paul y Claire Gardent. "Deep Semantics for Dependency Structures". En 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 completoActas de conferencias sobre el tema "Deep structures"
Chikersal, Prerna, Maria Tomprou, Young Ji Kim, Anita Williams Woolley y Laura Dabbish. "Deep Structures of Collaboration". En CSCW '17: Computer Supported Cooperative Work and Social Computing. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/2998181.2998250.
Texto completoGAWRONSKI, W., B. BIENKIEWICZ y R. HILL. "Wind-induced dynamics of the deep space network antennas". En 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 completoScalise, Carmen y Kevin Fitzpatrick. "Chicago Deep Tunnel Design and Construction". En Structures Congress 2012. Reston, VA: American Society of Civil Engineers, 2012. http://dx.doi.org/10.1061/9780784412367.132.
Texto completoHan, Jie y Ken Akins. "Use of Geogrid-Reinforced and Pile-Supported Earth Structures". En International Deep Foundations Congress 2002. Reston, VA: American Society of Civil Engineers, 2002. http://dx.doi.org/10.1061/40601(256)48.
Texto completoBirrcher, David, Robin Tuchscherer, Matthew Huizinga y Oguzhan Bayrak. "Depth Effect in Reinforced Concrete Deep Beams". En Structures Congress 2009. Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41031(341)175.
Texto completoBi, Zhnegfa y Xinming Wu. "Implicit structural modeling of geological structures with deep learning". En First International Meeting for Applied Geoscience & Energy. Society of Exploration Geophysicists, 2021. http://dx.doi.org/10.1190/segam2021-3583427.1.
Texto completoPhoon, Kok-Kwang y Fred H. Kulhawy. "Drilled Shaft Design for Transmission Structures Using LRFD and MRFD". En International Deep Foundations Congress 2002. Reston, VA: American Society of Civil Engineers, 2002. http://dx.doi.org/10.1061/40601(256)70.
Texto completoWu, Xiong-Jian y W. G. Price. "The Behaviour of Shallow Draft Offshore Structures and Service Vessels in Deeper Water". En Development In Deep Waters. RINA, 1986. http://dx.doi.org/10.3940/rina.ddw.1986.17.
Texto completoBouadi, Hakim, Eric Green y Narendra Gosain. "Evaluation and Repair of a Deep Transfer Girder". En Structures Congress 2005. Reston, VA: American Society of Civil Engineers, 2005. http://dx.doi.org/10.1061/40753(171)255.
Texto completoTomaszkiewicz, Karolina y Tomasz Owerko. "Deep machine learning in bridge structures durability analysis". En 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 completoInformes sobre el tema "Deep structures"
Harris, L. B., P. Adiban y 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 completoClay, 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 completoVIGIL, 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 completoBernau, Jeremiah A., Charles G. Oviatt, Donald L. Clark y 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, junio de 2023. http://dx.doi.org/10.34191/ofr-754.
Texto completoVito, L. F. Di, G. Mannucci, G. Demofonti, G. Cumino, A. Izquierdo, F. Daguerre, H. Quintanille y 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 completoBourhrous, Amal, Shivan Fazil y Dylan O’Driscoll. Post-conflict Reconstruction in the Nineveh Plains of Iraq: Agriculture, Cultural Practices and Social Cohesion. Stockholm International Peace Research Institute, noviembre de 2022. http://dx.doi.org/10.55163/raep9560.
Texto completoNg, Andrew Y. y Christopher D. Manning. Discovery of Deep Structure from Unlabeled Data. Fort Belvoir, VA: Defense Technical Information Center, noviembre de 2014. http://dx.doi.org/10.21236/ada614158.
Texto completoHeaney, Kevin. Spatial Structure of Deep Water Acoustic Propagation. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2008. http://dx.doi.org/10.21236/ada533364.
Texto completoDafoe, L. T., K. Dickie y 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 completoCopeland, Ronald y 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.
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