Relevant bibliographies by topics / Deep learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Deep learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Deep learning"

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Chagas, Edgar Thiago De Oliveira. "Deep Learning e suas aplicações na atualidade." Revista Científica Multidisciplinar Núcleo do Conhecimento 04, no. 05 (2019): 05–26. http://dx.doi.org/10.32749/nucleodoconhecimento.com.br/administracao/deep-learning.

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Wang, Yipu, and Stuart Perrin. "Deep Chinese Teaching and Learning Model Based on Deep Learning." International Journal of Languages, Literature and Linguistics 10, no. 1 (2024): 32–35. http://dx.doi.org/10.18178/ijlll.2024.10.1.479.

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Deep learning is a more situational and reflective way of learning that integrates complex knowledge and skills into intuitive thinking. As a language that closely combines sound, form and meaning, Chinese teaching and learning from the perspective of deep learning can help break through the limitations of the current teaching model that only focuses on certain language knowledge or cultural behaviors. This paper combines deep learning with international Chinese education, creates deep Chinese teaching and learning model including “four stages and ten steps”, and carries out practical applicat
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Jaiswal, Tarun, and Sushma Jaiswal. "Deep Learning in Medicine." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (2019): 212–17. http://dx.doi.org/10.31142/ijtsrd23641.

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Chagas, Edgar Thiago De Oliveira. "Deep Learning and its applications today." Revista Científica Multidisciplinar Núcleo do Conhecimento 04, no. 05 (2019): 05–26. http://dx.doi.org/10.32749/nucleodoconhecimento.com.br/business-administration/deep-learning-2.

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Jaiswal, Tarun, and Sushma Jaiswal. "Deep Learning Based Pain Treatment." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (2019): 193–211. http://dx.doi.org/10.31142/ijtsrd23639.

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Athani Samarth Kumar, Abusufiyan. "Cryptocurrency Prediction using Deep Learning." International Journal of Science and Research (IJSR) 12, no. 3 (2023): 1253–57. http://dx.doi.org/10.21275/sr23319215511.

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Bhadiyadra, Yash. "Object Detection with Deep Learning." International Journal of Science and Research (IJSR) 12, no. 7 (2023): 1300–1304. http://dx.doi.org/10.21275/mr23717204529.

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P C, Haris, and Dr Srikanth V. "Smart Eye Using Deep Learning." International Journal of Research Publication and Reviews 5, no. 3 (2024): 467–70. http://dx.doi.org/10.55248/gengpi.5.0324.0615.

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Zitar, Raed Abu, Ammar EL-Hassan, and Oraib AL-Sahlee. "Deep Learning Recommendation System for Course Learning Outcomes Assessment." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10-SPECIAL ISSUE (2019): 1491–78. http://dx.doi.org/10.5373/jardcs/v11sp10/20192993.

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Akgül, İsmail, and Yıldız Aydın. "OBJECT RECOGNITION WITH DEEP LEARNING AND MACHINE LEARNING METHODS." NWSA Academic Journals 17, no. 4 (2022): 54–61. http://dx.doi.org/10.12739/nwsa.2022.17.4.2a0189.

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Dissertations / Theses on the topic "Deep learning"

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Dufourq, Emmanuel. "Evolutionary deep learning." Doctoral thesis, Faculty of Science, 2019. http://hdl.handle.net/11427/30357.

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The primary objective of this thesis is to investigate whether evolutionary concepts can improve the performance, speed and convenience of algorithms in various active areas of machine learning research. Deep neural networks are exhibiting an explosion in the number of parameters that need to be trained, as well as the number of permutations of possible network architectures and hyper-parameters. There is little guidance on how to choose these and brute-force experimentation is prohibitively time consuming. We show that evolutionary algorithms can help tame this explosion of freedom, by develo
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He, Fengxiang. "Theoretical Deep Learning." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25674.

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Deep learning has long been criticised as a black-box model for lacking sound theoretical explanation. During the PhD course, I explore and establish theoretical foundations for deep learning. In this thesis, I present my contributions positioned upon existing literature: (1) analysing the generalizability of the neural networks with residual connections via complexity and capacity-based hypothesis complexity measures; (2) modeling stochastic gradient descent (SGD) by stochastic differential equations (SDEs) and their dynamics, and further characterizing the generalizability of deep learning;
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FRACCAROLI, MICHELE. "Explainable Deep Learning." Doctoral thesis, Università degli studi di Ferrara, 2023. https://hdl.handle.net/11392/2503729.

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Il grande successo che il Deep Learning ha ottenuto in ambiti strategici per la nostra società quali l'industria, la difesa, la medicina etc., ha portanto sempre più realtà a investire ed esplorare l'utilizzo di questa tecnologia. Ormai si possono trovare algoritmi di Machine Learning e Deep Learning quasi in ogni ambito della nostra vita. Dai telefoni, agli elettrodomestici intelligenti fino ai veicoli che guidiamo. Quindi si può dire che questa tecnologia pervarsiva è ormai a contatto con le nostre vite e quindi dobbiamo confrontarci con essa. Da questo nasce l’eXplainable Artificial Intelli
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Halle, Alex, and Alexander Hasse. "Topologieoptimierung mittels Deep Learning." Technische Universität Chemnitz, 2019. https://monarch.qucosa.de/id/qucosa%3A34343.

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Die Topologieoptimierung ist die Suche einer optimalen Bauteilgeometrie in Abhängigkeit des Einsatzfalls. Für komplexe Probleme kann die Topologieoptimierung aufgrund eines hohen Detailgrades viel Zeit- und Rechenkapazität erfordern. Diese Nachteile der Topologieoptimierung sollen mittels Deep Learning reduziert werden, so dass eine Topologieoptimierung dem Konstrukteur als sekundenschnelle Hilfe dient. Das Deep Learning ist die Erweiterung künstlicher neuronaler Netzwerke, mit denen Muster oder Verhaltensregeln erlernt werden können. So soll die bislang numerisch berechnete Topologieoptimieru
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Goh, Hanlin. "Learning deep visual representations." Paris 6, 2013. http://www.theses.fr/2013PA066356.

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Les avancées récentes en apprentissage profond et en traitement d'image présentent l'opportunité d'unifier ces deux champs de recherche complémentaires pour une meilleure résolution du problème de classification d'images dans des catégories sémantiques. L'apprentissage profond apporte au traitement d'image le pouvoir de représentation nécessaire à l'amélioration des performances des méthodes de classification d'images. Cette thèse propose de nouvelles méthodes d'apprentissage de représentations visuelles profondes pour la résolution de cette tache. L'apprentissage profond a été abordé sous deu
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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.

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Monte Carlo methods in derivative pricing are computationally expensive, in particular for evaluating models partial derivatives with regard to inputs. This research proposes the use of deep learning to approximate such valuation models for highly exotic derivatives, using automatic differentiation to evaluate input sensitivities. Deep learning models are trained to approximate Phoenix Autocall valuation using a proprietary model used by Svenska Handelsbanken AB. Models are trained on large datasets of low-accuracy (10^4 simulations) Monte Carlo data, successfully learning the true model with
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Wülfing, Jan [Verfasser], and Martin [Akademischer Betreuer] Riedmiller. "Stable deep reinforcement learning." Freiburg : Universität, 2019. http://d-nb.info/1204826188/34.

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White, Martin. "Deep Learning Software Repositories." W&M ScholarWorks, 2017. https://scholarworks.wm.edu/etd/1516639667.

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Bridging the abstraction gap between artifacts and concepts is the essence of software engineering (SE) research problems. SE researchers regularly use machine learning to bridge this gap, but there are three fundamental issues with traditional applications of machine learning in SE research. Traditional applications are too reliant on labeled data. They are too reliant on human intuition, and they are not capable of learning expressive yet efficient internal representations. Ultimately, SE research needs approaches that can automatically learn representations of massive, heterogeneous, datase
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Sun, Haozhe. "Modularity in deep learning." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG090.

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L'objectif de cette thèse est de rendre l'apprentissage profond plus efficace en termes de ressources en appliquant le principe de modularité. La thèse comporte plusieurs contributions principales : une étude de la littérature sur la modularité dans l'apprentissage profond; la conception d'OmniPrint et de Meta-Album, des outils qui facilitent l'étude de la modularité des données; des études de cas examinant les effets de l'apprentissage épisodique, un exemple de modularité des données; un mécanisme d'évaluation modulaire appelé LTU pour évaluer les risques en matière de protection de la vie pr
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Arnold, Ludovic. "Learning Deep Representations : Toward a better new understanding of the deep learning paradigm." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00842447.

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Since 2006, deep learning algorithms which rely on deep architectures with several layers of increasingly complex representations have been able to outperform state-of-the-art methods in several settings. Deep architectures can be very efficient in terms of the number of parameters required to represent complex operations which makes them very appealing to achieve good generalization with small amounts of data. Although training deep architectures has traditionally been considered a difficult problem, a successful approach has been to employ an unsupervised layer-wise pre-training step to init
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Books on the topic "Deep learning"

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Saefken, Benjamin, Alexander Silbersdorff, and Christoph Weisser, eds. Learning deep. Göttingen University Press, 2020. http://dx.doi.org/10.17875/gup2020-1338.

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Bishop, Christopher M., and Hugh Bishop. Deep Learning. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-45468-4.

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Kruse, René-Marcel, Benjamin Säfken, Alexander Silbersdorff, and Christoph Weisser, eds. Learning Deep Textwork. Göttingen University Press, 2021. http://dx.doi.org/10.17875/gup2021-1608.

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Rodriguez, Andres. Deep Learning Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-031-01769-8.

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Fergus, Paul, and Carl Chalmers. Applied Deep Learning. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04420-5.

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Calin, Ovidiu. Deep Learning Architectures. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36721-3.

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El-Amir, Hisham, and Mahmoud Hamdy. Deep Learning Pipeline. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5349-6.

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Matsushita, Kayo, ed. Deep Active Learning. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5660-4.

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Michelucci, Umberto. Applied Deep Learning. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3790-8.

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Moons, Bert, Daniel Bankman, and Marian Verhelst. Embedded Deep Learning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-99223-5.

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Book chapters on the topic "Deep learning"

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Meedeniya, Dulani. "State-of-the-Art Deep Learning Models: Part I." In Deep Learning. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003390824-3.

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Meedeniya, Dulani. "Concepts and Terminology." In Deep Learning. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003390824-2.

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Meedeniya, Dulani. "Introduction." In Deep Learning. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003390824-1.

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Meedeniya, Dulani. "State-of-the-Art Deep Learning Models: Part II." In Deep Learning. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003390824-4.

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Meedeniya, Dulani. "Performance Evaluation Techniques." In Deep Learning. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003390824-7.

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Meedeniya, Dulani. "Enhancement of Deep Learning Architectures." In Deep Learning. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003390824-6.

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Meedeniya, Dulani. "Advanced Learning Techniques." In Deep Learning. Chapman and Hall/CRC, 2023. http://dx.doi.org/10.1201/9781003390824-5.

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Bishop, Christopher M., and Hugh Bishop. "Graph Neural Networks." In Deep Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-45468-4_13.

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Bishop, Christopher M., and Hugh Bishop. "Convolutional Networks." In Deep Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-45468-4_10.

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Bishop, Christopher M., and Hugh Bishop. "Single-layer Networks: Classification." In Deep Learning. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-45468-4_5.

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Conference papers on the topic "Deep learning"

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Yao, Jenq-Foung, Yu-Hsiang John Huang, Cheng-Ying Yang, and Min-Shiang Hwang. "Deep Learning Applications." In 2024 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS). IEEE, 2024. https://doi.org/10.1109/ispacs62486.2024.10869071.

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Baresi, Luciano, Davide Yi Xian Hu, Muhammad Irfan Mas’udi, and Giovanni Quattrocchi. "DILLEMA: Diffusion and Large Language Models for Multi-Modal Augmentation." In 2025 IEEE/ACM International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest). IEEE, 2025. https://doi.org/10.1109/deeptest66595.2025.00010.

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Kim, Somin, and Shin Yoo. "DANDI: Diffusion as Normative Distribution for Deep Neural Network Input." In 2025 IEEE/ACM International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest). IEEE, 2025. https://doi.org/10.1109/deeptest66595.2025.00007.

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Peixoto, Myron, Davy Baía, Nathalia Nascimento, Paulo Alencar, Baldoino Fonseca, and Márcio Ribeiro. "On the Effectiveness of LLMs for Manual Test Verifications." In 2025 IEEE/ACM International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest). IEEE, 2025. https://doi.org/10.1109/deeptest66595.2025.00012.

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Lim, Gordon, Stefan Larson, and Kevin Leach. "Robust Testing for Deep Learning using Human Label Noise." In 2025 IEEE/ACM International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest). IEEE, 2025. https://doi.org/10.1109/deeptest66595.2025.00009.

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Ali, Qurban, Andrea Stocco, Leonardo Mariani, and Oliviero Riganelli. "OpenCat: Improving Interoperability of ADS Testing." In 2025 IEEE/ACM International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest). IEEE, 2025. https://doi.org/10.1109/deeptest66595.2025.00013.

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Kim, Naryeong, Sungmin Kang, Gabin An, and Shin Yoo. "Lachesis: Predicting LLM Inference Accuracy using Structural Properties of Reasoning Paths." In 2025 IEEE/ACM International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest). IEEE, 2025. https://doi.org/10.1109/deeptest66595.2025.00008.

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Poenaru-Olaru, Lorena, Luis Cruz, Jan S. Rellermeyer, and Arie van Deursen. "Improving the Reliability of Failure Prediction Models through Concept Drift Monitoring." In 2025 IEEE/ACM International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest). IEEE, 2025. https://doi.org/10.1109/deeptest66595.2025.00006.

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Steenhoek, Benjamin, Michele Tufano, Neel Sundaresan, and Alexey Svyatkovskiy. "Reinforcement Learning from Automatic Feedback for High-Quality Unit Test Generation." In 2025 IEEE/ACM International Workshop on Deep Learning for Testing and Testing for Deep Learning (DeepTest). IEEE, 2025. https://doi.org/10.1109/deeptest66595.2025.00011.

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"DEEP-ML 2019 Program Committee." In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 2019. http://dx.doi.org/10.1109/deep-ml.2019.00007.

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Reports on the topic "Deep learning"

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Catanach, Thomas, and Jed Duersch. Efficient Generalizable Deep Learning. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1760400.

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Dell, Melissa. Deep Learning for Economists. National Bureau of Economic Research, 2024. http://dx.doi.org/10.3386/w32768.

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Groh, Micah. NOvA Reconstruction using Deep Learning. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1462092.

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Geiss, Andrew, Joseph Hardin, Sam Silva, William Jr., Adam Varble, and Jiwen Fan. Deep Learning for Ensemble Forecasting. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769692.

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Harris, James, Shannon Kinkead, Dylan Fox, and Yang Ho. Continual Learning for Pattern Recognizers using Neurogenesis Deep Learning. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1855019.

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Draelos, Timothy John, Nadine E. Miner, Christopher C. Lamb, et al. Neurogenesis Deep Learning: Extending deep networks to accommodate new classes. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1505351.

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Balaji, Praveen. Detecting Stellar Streams through Deep Learning. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1637622.

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Li, Li. Deep Learning for Hydro-Biogeochemistry Processes. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769693.

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Eydenberg, Michael, Lisa Batsch-Smith, Charles Bice, et al. Resilience Enhancements through Deep Learning Yields. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1890044.

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Oskolkov, Nikolay. Deep Learning for the Life Sciences. Instats Inc., 2024. https://doi.org/10.61700/zjxxse1x3u05y1846.

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This intensive workshop provides a comprehensive exploration of deep learning applications in life sciences, focusing on practical techniques for analyzing complex biological datasets. Participants will gain theoretical and hands-on experience with deep learning tools such as TensorFlow and Keras, learning to construct neural networks and apply them to areas like genomics and personalized medicine.
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