Thèses sur le sujet « DEE9 »
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MAZZOLENI, SARA. « SOLVING THE PUZZLE OF PROTOCADHERIN-19 MOSAICISM TO UNDERSTAND THE PATHOPHYSIOLOGY OF DEVELOPMENTAL AND EPILEPTIC ENCEPHALOPATHY 9 (DEE9) ». Doctoral thesis, Università degli Studi di Milano, 2022. http://hdl.handle.net/2434/918930.
Texte intégralWestphal, Robert. « Effect of unilateral neurodegeneration on brain morphology, connectivity and pharmacology in Parkinson's disease rat models : a multimodal MRI study with corroborative techniques ». Thesis, King's College London (University of London), 2016. https://kclpure.kcl.ac.uk/portal/en/theses/effect-of-unilateral-neurodegeneration-on-brain-morphology-connectivity-and-pharmacology-in-parkinsons-disease-rat-models(41d9dc8b-dee9-4a64-a173-bf5bfd207941).html.
Texte intégralAllen, James Robert. « The structure, function and specificity of the Rhodobacter sphaeroides membrane-associated chemotaxis array ». Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:ce7de07a-dee6-471b-9f70-22714e617693.
Texte intégralPeralta, Yaddyra. « Deep Waters ». FIU Digital Commons, 2012. http://digitalcommons.fiu.edu/etd/622.
Texte intégralStraube, Nicolas. « Deep divergence ». Diss., Ludwig-Maximilians-Universität München, 2011. http://nbn-resolving.de/urn:nbn:de:bvb:19-138186.
Texte intégralJoseph, Caberbe. « DEEP WITHIN ». Master's thesis, University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2794.
Texte intégralM.F.A.
Department of Art
Arts and Humanities
Studio Art and the Computer MFA
Krotevych, K. « Deep web ». Thesis, Sumy State University, 2015. http://essuir.sumdu.edu.ua/handle/123456789/40487.
Texte intégralWood, Rebecca. « Deep Surface ». University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1427899904.
Texte intégralPeterson, Grant. « Deep time / ». abstract, 2008. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1455664.
Texte intégral"May, 2008." Library also has microfilm. Ann Arbor, Mich. : ProQuest Information and Learning Company, [2009]. 1 microfilm reel ; 35 mm. Online version available on the World Wide Web.
Traxl, Dominik. « Deep graphs ». Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2017. http://dx.doi.org/10.18452/17785.
Texte intégralNetwork theory has proven to be a powerful instrument in the representation of complex systems. Yet, even in its latest and most general form (i.e., multilayer networks), it is still lacking essential qualities to serve as a general data analysis framework. These include, most importantly, an explicit association of information with the nodes and edges of a network, and a conclusive representation of groups of nodes and their respective interrelations on different scales. The implementation of these qualities into a generalized framework is the primary contribution of this dissertation. By doing so, I show how my framework - deep graphs - is capable of acting as a go-between, joining a unified and generalized network representation of systems with the tools and methods developed in statistics and machine learning. A software package accompanies this dissertation, see https://github.com/deepgraph/deepgraph. A number of applications of my framework are demonstrated. I construct a rainfall deep graph and conduct an analysis of spatio-temporal extreme rainfall clusters. Based on the constructed deep graph, I provide statistical evidence that the size distribution of these clusters is best approximated by an exponentially truncated powerlaw. By means of a generative storm-track model, I argue that the exponential truncation of the observed distribution could be caused by the presence of land masses. Then, I combine two high-resolution satellite products to identify spatio-temporal clusters of fire-affected areas in the Brazilian Amazon and characterize their land use specific burning conditions. Finally, I investigate the effects of white noise and global coupling strength on the maximum degree of synchronization for a variety of oscillator models coupled according to a broad spectrum of network topologies. I find a general sigmoidal scaling and validate it with a suitable regression model.
Jönsson, Jennifer Annie Patricia. « Deep Impression ». Thesis, Högskolan i Borås, Akademin för textil, teknik och ekonomi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-22025.
Texte intégralSeraku, Tohru. « Clefts, relatives, and language dynamics : the case of Japanese ». Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:0448acc3-dee6-4b1b-9020-95fd84895f24.
Texte intégralHope, Kofi N. « In search of solidarity : international solidarity work between Canada and South Africa 1975-2010 ». Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:94fc88ca-de19-4e97-b66f-97cd9f5d4595.
Texte intégralChitengi, Howard S. « Deriving lessons for urban planning and housing delivery from the resilience of informal housing systems in Zambia ». Thesis, University of Dundee, 2015. https://discovery.dundee.ac.uk/en/studentTheses/a0f283ed-de59-4b8f-89d8-5380a9b919ae.
Texte intégralHuang, Ruobing. « Delving deep into fetal neurosonography : an image analysis approach ». Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:63aec035-dee2-40d4-9e00-ee1674a52494.
Texte intégralCusack, Martin. « The role of DNA methylation on transcription factor occupancy and transcriptional activity ». Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:7d0b7fe7-dee1-433f-8656-c9ee2a216d48.
Texte intégralLynch, Cassie A. « Korangan : Deep Time and Deep Transformation in Noongar Country ». Thesis, Curtin University, 2020. http://hdl.handle.net/20.500.11937/81989.
Texte intégralBackstad, Sebastian. « Federated Averaging Deep Q-NetworkA Distributed Deep Reinforcement Learning Algorithm ». Thesis, Umeå universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-149637.
Texte intégralDunlop, J. S., R. J. McLure, A. D. Biggs, J. E. Geach, M. J. Michałowski, R. J. Ivison, W. Rujopakarn et al. « A deep ALMA image of the Hubble Ultra Deep Field ». OXFORD UNIV PRESS, 2017. http://hdl.handle.net/10150/623849.
Texte intégralCarvalho, Micael. « Deep representation spaces ». Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS292.
Texte intégralIn recent years, Deep Learning techniques have swept the state-of-the-art of many applications of Machine Learning, becoming the new standard approach for them. The architectures issued from these techniques have been used for transfer learning, which extended the power of deep models to tasks that did not have enough data to fully train them from scratch. This thesis' subject of study is the representation spaces created by deep architectures. First, we study properties inherent to them, with particular interest in dimensionality redundancy and precision of their features. Our findings reveal a strong degree of robustness, pointing the path to simple and powerful compression schemes. Then, we focus on refining these representations. We choose to adopt a cross-modal multi-task problem, and design a loss function capable of taking advantage of data coming from multiple modalities, while also taking into account different tasks associated to the same dataset. In order to correctly balance these losses, we also we develop a new sampling scheme that only takes into account examples contributing to the learning phase, i.e. those having a positive loss. Finally, we test our approach in a large-scale dataset of cooking recipes and associated pictures. Our method achieves a 5-fold improvement over the state-of-the-art, and we show that the multi-task aspect of our approach promotes a semantically meaningful organization of the representation space, allowing it to perform subtasks never seen during training, like ingredient exclusion and selection. The results we present in this thesis open many possibilities, including feature compression for remote applications, robust multi-modal and multi-task learning, and feature space refinement. For the cooking application, in particular, many of our findings are directly applicable in a real-world context, especially for the detection of allergens, finding alternative recipes due to dietary restrictions, and menu planning
Carter, Justin Ryan. « Assume Deer Dead ». Bowling Green State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1395065120.
Texte intégralDufourq, Emmanuel. « Evolutionary deep learning ». Doctoral thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/30357.
Texte intégralHe, Fengxiang. « Theoretical Deep Learning ». Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25674.
Texte intégralManna, Amin(Amin A. ). « Deep linguistic lensing ». Thesis, Massachusetts Institute of Technology, 2018. https://hdl.handle.net/1721.1/121630.
Texte intégralThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 81-84).
Language models and semantic word embeddings have become ubiquitous as sources for machine learning features in a wide range of predictive tasks and real-world applications. We argue that language models trained on a corpus of text can learn the linguistic biases implicit in that corpus. We discuss linguistic biases, or differences in identity and perspective that account for the variation in language use from one speaker to another. We then describe methods to intentionally capture "linguistic lenses": computational representations of these perspectives. We show how the captured lenses can be used to guide machine learning models during training. We define a number of lenses for author-to-author similarity and word-to-word interchangeability. We demonstrate how lenses can be used during training time to imbue language models with perspectives about writing style, or to create lensed language models that learn less linguistic gender bias than their un-lensed counterparts.
by Amin Manna.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
FRACCAROLI, MICHELE. « Explainable Deep Learning ». Doctoral thesis, Università degli studi di Ferrara, 2023. https://hdl.handle.net/11392/2503729.
Texte intégralThe great success that Machine and Deep Learning has achieved in areas that are strategic for our society such as industry, defence, medicine, etc., has led more and more realities to invest and explore the use of this technology. Machine Learning and Deep Learning algorithms and learned models can now be found in almost every area of our lives. From phones to smart home appliances, to the cars we drive. So it can be said that this pervasive technology is now in touch with our lives, and therefore we have to deal with it. This is why eXplainable Artificial Intelligence or XAI was born, one of the research trends that are currently in vogue in the field of Deep Learning and Artificial Intelligence. The idea behind this line of research is to make and/or design the new Deep Learning algorithms so that they are interpretable and comprehensible to humans. This necessity is due precisely to the fact that neural networks, the mathematical model underlying Deep Learning, act like a black box, making the internal reasoning they carry out to reach a decision incomprehensible and untrustable to humans. As we are delegating more and more important decisions to these mathematical models, it is very important to be able to understand the motivations that lead these models to make certain decisions. This is because we have integrated them into the most delicate processes of our society, such as medical diagnosis, autonomous driving or legal processes. The work presented in this thesis consists in studying and testing Deep Learning algorithms integrated with symbolic Artificial Intelligence techniques. This integration has a twofold purpose: to make the models more powerful, enabling them to carry out reasoning or constraining their behaviour in complex situations, and to make them interpretable. The thesis focuses on two macro topics: the explanations obtained through neuro-symbolic integration and the exploitation of explanations to make the Deep Learning algorithms more capable or intelligent. The neuro-symbolic integration was addressed twice, by experimenting with the integration of symbolic algorithms with neural networks. A first approach was to create a system to guide the training of the networks themselves in order to find the best combination of hyper-parameters to automate the design of these networks. This is done by integrating neural networks with Probabilistic Logic Programming (PLP). This integration makes it possible to exploit probabilistic rules tuned by the behaviour of the networks during the training phase or inherited from the experience of experts in the field. These rules are triggered when a problem occurs during network training. This generates an explanation of what was done to improve the training once a particular issue was identified. A second approach was to make probabilistic logic systems cooperate with neural networks for medical diagnosis on heterogeneous data sources. The second topic addressed in this thesis concerns the exploitation of explanations. In particular, the explanations one can obtain from neural networks are used in order to create attention modules that help in constraining and improving the performance of neural networks. All works developed during the PhD and described in this thesis have led to the publications listed in Chapter 14.2.
Hervert, John Joseph. « Mule deer use of water developments in Arizona ». Thesis, The University of Arizona, 1985. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu_e9791_1985_270_sip1_w.pdf&type=application/pdf.
Texte intégralDan-Jumbo, F. G. « Material and structural properties of a novel Aer-Tech material ». Thesis, Coventry University, 2015. http://curve.coventry.ac.uk/open/items/699ca3a1-deec-4549-b907-0e06bcdad83f/1.
Texte intégralParker, Israel David. « Effects of translocation and deer-vehicle collision mitigation on Florida Key deer ». [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1878.
Texte intégralMarchesini, Gregorio. « Caratterizzazione della Sardinia Deep Space Antenna in supporto di missioni deep space ». Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20809/.
Texte intégralStorm, Daniel J. « White-tailed deer ecology and deer-human conflict in an exurban landscape / ». Available to subscribers only, 2005. http://proquest.umi.com/pqdweb?did=1095427551&sid=6&Fmt=2&clientId=1509&RQT=309&VName=PQD.
Texte intégralBenge, Sarah Elizabeth. « Nutrient selection by fallow deer (Dama dama) and roe deer (Capreolus capreolus) ». Thesis, University of Southampton, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.249639.
Texte intégralPerjeru, Florentine. « Deep Defects in Wide Bandgap Materials Investigated Using Deep Level Transient Spectroscopy ». Ohio University / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou997365452.
Texte intégralMansour, Tarek M. Eng Massachusetts Institute of Technology. « Deep neural networks are lazy : on the inductive bias of deep learning ». Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121680.
Texte intégralThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 75-78).
Deep learning models exhibit superior generalization performance despite being heavily overparametrized. Although widely observed in practice, there is currently very little theoretical backing for such a phenomena. In this thesis, we propose a step forward towards understanding generalization in deep learning. We present evidence that deep neural networks have an inherent inductive bias that makes them inclined to learn generalizable hypotheses and avoid memorization. In this respect, we propose results that suggest that the inductive bias stems from neural networks being lazy: they tend to learn simpler rules first. We also propose a definition of simplicity in deep learning based on the implicit priors ingrained in deep neural networks.
by Tarek Mansour.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
Merkt, Juan R. « Social structure of Andean deer (Hippocamelus antisensis) in southern Peru ». Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/24864.
Texte intégralScience, Faculty of
Zoology, Department of
Graduate
Ebersole, Regina L. « Efficacy of a controlled hunt for managing white-tailed deer on Fair Hill Natural Resource Management Area, Cecil County, Maryland ». Access to citation, abstract and download form provided by ProQuest Information and Learning Company ; downloadable PDF file, 66 p, 2007. http://proquest.umi.com/pqdweb?did=1253509781&sid=1&Fmt=2&clientId=8331&RQT=309&VName=PQD.
Texte intégralDaniels, Kelly L. « Deep water, open water ». Master's thesis, Mississippi State : Mississippi State University, 2009. http://library.msstate.edu/etd/show.asp?etd=etd-04022009-163550.
Texte intégralBurchfield, Monica R. « Fish from Deep Water ». Digital Archive @ GSU, 2010. http://digitalarchive.gsu.edu/english_theses/100.
Texte intégralStone, Rebecca E. « Deep mixed layer entrainment ». Monterey, California. Naval Postgraduate School, 1997. http://hdl.handle.net/10945/8198.
Texte intégralA bulk turbulence-closure mixed layer model is generalized to allow prediction of very deep polar sea mixing. The model includes unsteady three- component turbulent kinetic energy budgets. In addition to terms for shear production, pressure redistribution, and dissipation, special attention is devoted to realistic treatment of thermobaric enhancement of buoyancy flux and to Coriolis effect on turbulence. The model is initialized and verified with CTD data taken by R/V Valdivia in the Greenland Sea during winter 1993-1994. Model simulations show (1) mixed layer deepening is significantly enhanced when the thermal expansion coefficient's increase with pressure is included; (2) entrainment rate is sensitive to the direction of wind stress because of Coriolis; and (3) the predicted mixed layer depth evolution agrees qualitatively with the observations. Results demonstrate the importance of water column initial conditions, accurate representation of strong surface cooling events, and inclusion of the thermobaric effect on buoyancy, to determine the depth of mixing and ultimately the heat and salt flux into the deep ocean. Since coupling of the ocean to the atmosphere through deep mixed layers in polar regions is fundamental to our climate system, it is important that regional and global models be developed that incorporate realistic representation of this coupling
Beyer, Franziska C. « Deep levels in SiC ». Doctoral thesis, Linköpings universitet, Halvledarmaterial, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70356.
Texte intégralLiu, Qian. « Deep spiking neural networks ». Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/deep-spiking-neural-networks(336e6a37-2a0b-41ff-9ffb-cca897220d6c).html.
Texte intégralSheiretov, Yanko Konstantinov. « Deep penetration magnetoquasistatic sensors ». Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/16772.
Texte intégralIncludes bibliographical references (p. 193-198).
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
This research effort extends the capabilities of existing model-based spatially periodic quasistatic-field sensors. The research developed three significant improvements in the field of nondestructive evaluation. The impact of each is detailed below: 1. The design of a distributed current drive magneto resistive magnetometer that matches the model response sufficiently to perform air calibration and absolute property measurement. Replacing the secondary winding with a magnetoresistive sensor allows the magnetometer to be operated at frequencies much lower than ordinarily possible, including static (DC) operation, which enables deep penetration defect imaging. Low frequencies are needed for deep probing of metals, where the depth of penetration is otherwise limited by the skin depth due to the shielding effect of induced eddy currents. The capability to perform such imaging without dependence on calibration standards has both substantial cost, ease of use, and technological benefits. The absolute property measurement capability is important because it provides a robust comparison for manufacturing quality control and monitoring of aging processes. Air calibration also alleviates the dependence on calibration standards that can be difficult to maintain. 2. The development and validation of cylindrical geometry models for inductive and capacitive sensors. The development of cylindrical geometry models enable the design of families of circularly symmetric magnetometers and dielectrometers with the "model-based" methodology, which requires close agreement between actual sensor response and simulated response. These kinds of sensors are needed in applications where the components being tested have circular symmetry, e.g. cracks near fasteners, or if it is important to measure the spatial average of an anisotropic property. 3. The development of accurate and efficient two-dimensional inverse interpolation and grid look-up techniques to determine electromagnetic and geometric properties. The ability to perform accurate and efficient grid interpolation is important for all sensors that follow the model-based principle, but it is particularly important for the complex shaped grids used with the magnetometers and dielectrometers in this thesis. A prototype sensor that incorporates all new features, i.e. a circularly symmetric magnetometer with a distributed current drive that uses a magnetoresistive secondary element, was designed, built, and tested. The primary winding is designed to have no net dipole moment, which improves repeatability by reducing the influence of distant objects. It can also support operation at two distinct effective spatial wavelengths. A circuit is designed that places the magnetoresistive sensor in a feedback configuration with a secondary winding to provide the necessary biasing and to ensure a linear transfer characteristic. Efficient FFT-based methods are developed to model magnetometers with a distributed current drive for both Cartesian and cylindrical geometry sensors. Results from measurements with a prototype circular dielectrometer that agree with the model-based analysis are also presented. In addition to the main contributions described so far, this work also includes other related enhancements to the time and space periodic-field sensor models, such as incorporating motion in the models to account for moving media effects. This development is important in low frequency scanning applications. Some improvements of the existing semi-analytical collocation point models for the standard Cartesian magnetometers and dielectrometers are also presented.
by Yanko Sheiretov.
Ph.D.
Börger, Luca. « Roe deer mating tactics ». Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.614310.
Texte intégralWaber, Kristin. « Landscape scale deer management ». Thesis, University of East Anglia, 2010. https://ueaeprints.uea.ac.uk/33047/.
Texte intégralPatil, Raj. « Deep UV Raman Spectroscopy ». Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/613378.
Texte intégralFahr, Mignon. « As Runs the Deer ». ScholarWorks@UNO, 2003. http://scholarworks.uno.edu/td/11.
Texte intégralDebain, Yann. « Deep Convolutional Nonnegative Autoencoders ». Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-287352.
Texte intégralI den här rapporten betraktas icke-negativ matrisfaktorisering (eng: nonnegative matrix factorization, NMF) som ett återkopplat neuralt nätverk. NMF är generaliserat till en djup faltningsarkitektur med “forwardpropagation” och β-divergens. NMF och “feedforward” neurala nät jämförs och en ny typ av autokodare är presenterat. Den nya typen av autokodare kallas icke-negativ autokodare. NMF betraktas avkodardelen av en autokodare med icke-negativa vikter och ingång. Den grunda autokodare med summationsdelen är utbyggd till en djup faltningsautokodare med icke-negativa vikter och ingång. I den här rapporten utvecklades en grund icke-negativ autokodare (eng: nonnegative autoencoder, NAE), en grund icke-negativ faltningsautokodare (eng: convolutional nonnegative autoencoder, CNAE) och en djup icke-negativ faltningsautokodare (eng: deep convolutional nonnegative autoencoder, DCNAE). Slutligen testas de tre varianterna av icke-negativ autokodare på några olika uppgifter som signalrekonstruktion och signalförbättring.
Halle, Alex, et Alexander Hasse. « Topologieoptimierung mittels Deep Learning ». Technische Universität Chemnitz, 2019. https://monarch.qucosa.de/id/qucosa%3A34343.
Texte intégralGoh, Hanlin. « Learning deep visual representations ». Paris 6, 2013. http://www.theses.fr/2013PA066356.
Texte intégralRecent advancements in the areas of deep learning and visual information processing have presented an opportunity to unite both fields. These complementary fields combine to tackle the problem of classifying images into their semantic categories. Deep learning brings learning and representational capabilities to a visual processing model that is adapted for image classification. This thesis addresses problems that lead to the proposal of learning deep visual representations for image classification. The problem of deep learning is tackled on two fronts. The first aspect is the problem of unsupervised learning of latent representations from input data. The main focus is the integration of prior knowledge into the learning of restricted Boltzmann machines (RBM) through regularization. Regularizers are proposed to induce sparsity, selectivity and topographic organization in the coding to improve discrimination and invariance. The second direction introduces the notion of gradually transiting from unsupervised layer-wise learning to supervised deep learning. This is done through the integration of bottom-up information with top-down signals. Two novel implementations supporting this notion are explored. The first method uses top-down regularization to train a deep network of RBMs. The second method combines predictive and reconstructive loss functions to optimize a stack of encoder-decoder networks. The proposed deep learning techniques are applied to tackle the image classification problem. The bag-of-words model is adopted due to its strengths in image modeling through the use of local image descriptors and spatial pooling schemes. Deep learning with spatial aggregation is used to learn a hierarchical visual dictionary for encoding the image descriptors into mid-level representations. This method achieves leading image classification performances for object and scene images. The learned dictionaries are diverse and non-redundant. The speed of inference is also high. From this, a further optimization is performed for the subsequent pooling step. This is done by introducing a differentiable pooling parameterization and applying the error backpropagation algorithm. This thesis represents one of the first attempts to synthesize deep learning and the bag-of-words model. This union results in many challenging research problems, leaving much room for further study in this area
Geirsson, Gunnlaugur. « Deep learning exotic derivatives ». Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-430410.
Texte intégralWolfe, Traci. « Digging deep for meaning ». Online version, 2008. http://www.uwstout.edu/lib/thesis/2008/2008wolfet.pdf.
Texte intégral