Bibliografie tematiche / Self-supervised learning (artificial intelligence)

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Letteratura scientifica selezionata sul tema "Self-supervised learning (artificial intelligence)"

Autore: Grafiati

Pubblicato: 7 luglio 2024

Ultima modifica: 31 luglio 2025

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Articoli di riviste sul tema "Self-supervised learning (artificial intelligence)"

Neghawi, Elie, and Yan Liu. "Enhancing Self-Supervised Learning through Explainable Artificial Intelligence Mechanisms: A Computational Analysis." Big Data and Cognitive Computing 8, no. 6 (2024): 58. http://dx.doi.org/10.3390/bdcc8060058.

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Self-supervised learning continues to drive advancements in machine learning. However, the absence of unified computational processes for benchmarking and evaluation remains a challenge. This study conducts a comprehensive analysis of state-of-the-art self-supervised learning algorithms, emphasizing their underlying mechanisms and computational intricacies. Building upon this analysis, we introduce a unified model-agnostic computation (UMAC) process, tailored to complement modern self-supervised learning algorithms. UMAC serves as a model-agnostic and global explainable artificial intelligence

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Manal, Al-otaibi. "Training and Artificial Intelligence." International Journal of Social Science and Humanities Research 12, no. 3 (2024): 166–76. https://doi.org/10.5281/zenodo.13268751.

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Abstract: Artificial intelligence is a branch of science that is growing at a fast rate to make computers behave like humans. Training and AI is one of the tech advancements that cover a wide range of areas like neural networks, natural language processing, gaming, robotics, and automation. Today, neural networks, computing, and automation are some of the hottest areas in AI that are successfully implemented in areas like natural language processing and voice recognition. This research paper explores the intersection of training and artificial intelligence (AI) and its implica

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CHAN, JASON, IRENA KOPRINSKA, and JOSIAH POON. "SEMI-SUPERVISED CLASSIFICATION USING BRIDGING." International Journal on Artificial Intelligence Tools 17, no. 03 (2008): 415–31. http://dx.doi.org/10.1142/s0218213008003972.

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Traditional supervised classification algorithms require a large number of labelled examples to perform accurately. Semi-supervised classification algorithms attempt to overcome this major limitation by also using unlabelled examples. Unlabelled examples have also been used to improve nearest neighbour text classification in a method called bridging. In this paper, we propose the use of bridging in a semi-supervised setting. We introduce a new bridging algorithm that can be used as a base classifier in most semi-supervised approaches. We empirically show that the classification performance of

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Yuya, KOBAYASHI, Masahiro SUZUKI, and Yutaka MATSUO. "Scene Interpretation Method using Transformer and Self-supervised Learning." Transactions of the Japanese Society for Artificial Intelligence 37, no. 2 (2022): I—L75_1–17. http://dx.doi.org/10.1527/tjsai.37-2_i-l75.

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Hrycej, Tomas. "Supporting supervised learning by self-organization." Neurocomputing 4, no. 1-2 (1992): 17–30. http://dx.doi.org/10.1016/0925-2312(92)90040-v.

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Wang, Fei, and Changshui Zhang. "Robust self-tuning semi-supervised learning." Neurocomputing 70, no. 16-18 (2007): 2931–39. http://dx.doi.org/10.1016/j.neucom.2006.11.004.

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Biscione, Valerio, and Jeffrey S. Bowers. "Learning online visual invariances for novel objects via supervised and self-supervised training." Neural Networks 150 (June 2022): 222–36. http://dx.doi.org/10.1016/j.neunet.2022.02.017.

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Wei, Kaibin, Haifeng Li, Qing Liu, and Xiongjian Zhang. "Self-Supervised, Multi-View, Semantics-Aware Anchor Clustering." Electronics 13, no. 23 (2024): 4782. https://doi.org/10.3390/electronics13234782.

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Data-driven artificial intelligence systems effectively enhance accuracy and robustness by utilizing multi-view learning to aggregate consistent and complementary information from multi-source data. As one of the most important branches of multi-view learning, multi-view anchor clustering greatly reduces the time complexity via learning similarity graphs between anchors and data instead of data-to-data similarities, which has gained widespread attention in data-driven artificial intelligence domains. However, two issues still exist in current methods: (1) They commonly utilize orthogonal regul

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Ma, Jun, Yakun Wen, and Liming Yang. "Lagrangian supervised and semi-supervised extreme learning machine." Applied Intelligence 49, no. 2 (2018): 303–18. http://dx.doi.org/10.1007/s10489-018-1273-4.

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Vo Khuong Linh and Nguyen Hoa Nhat Quang. "Malware detection in PE files using deep learning with self-supervised learning techniques." Tạp chí Khoa học Lạc Hồng 1, no. 20 (2025): 6–12. https://doi.org/10.61591/jslhu.20.608.

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In recent years, there has been a surge in new malware created by hackers globally, posing challenges for traditional detection methods. This paper explores using advanced artificial intelligence, specifically Deep Learning with Self-supervised learning, to identify malware in executable files. Our study focuses on comparing the effectiveness of popular deep learning techniques like CNN models and fine-tuned CNN models, against Autoencoder models. The key contribution of this paper lies in comparing the results of these different approaches to malware detection.

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Tesi sul tema "Self-supervised learning (artificial intelligence)"

Denize, Julien. "Self-supervised representation learning and applications to image and video analysis." Electronic Thesis or Diss., Normandie, 2023. http://www.theses.fr/2023NORMIR37.

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Dans cette thèse, nous développons des approches d'apprentissage auto-supervisé pour l'analyse d'images et de vidéos. L'apprentissage de représentation auto-supervisé permet de pré-entraîner les réseaux neuronaux à apprendre des concepts généraux sans annotations avant de les spécialiser plus rapidement à effectuer des tâches, et avec peu d'annotations. Nous présentons trois contributions à l'apprentissage auto-supervisé de représentations d'images et de vidéos. Premièrement, nous introduisons le paradigme théorique de l'apprentissage contrastif doux et sa mise en œuvre pratique appelée Estima

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Nett, Ryan. "Dataset and Evaluation of Self-Supervised Learning for Panoramic Depth Estimation." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2234.

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Depth detection is a very common computer vision problem. It shows up primarily in robotics, automation, or 3D visualization domains, as it is essential for converting images to point clouds. One of the poster child applications is self driving cars. Currently, the best methods for depth detection are either very expensive, like LIDAR, or require precise calibration, like stereo cameras. These costs have given rise to attempts to detect depth from a monocular camera (a single camera). While this is possible, it is harder than LIDAR or stereo methods since depth can't be measured from monocular

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Stanescu, Ana. "Semi-supervised learning for biological sequence classification." Diss., Kansas State University, 2015. http://hdl.handle.net/2097/35810.

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Doctor of Philosophy Department of Computing and Information Sciences Doina Caragea Successful advances in biochemical technologies have led to inexpensive, time-efficient production of massive volumes of data, DNA and protein sequences. As a result, numerous computational methods for genome annotation have emerged, including machine learning and statistical analysis approaches that practically and efficiently analyze and interpret data. Traditional machine learning approaches to genome annotation typically rely on large amounts of labeled data in order to build quality classifiers. T

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Abou-Moustafa, Karim. "Metric learning revisited: new approaches for supervised and unsupervised metric learning with analysis and algorithms." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=106370.

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In machine learning one is usually given a data set of real high dimensional vectors X, based on which it is desired to select a hypothesis θ from the space of hypotheses Θ using a learning algorithm. An immediate assumption that is usually imposed on X is that it is a subset from the very general embedding space Rp which makes the Euclidean distance ∥•∥2 to become the default metric for the elements of X. Since various learning algorithms assume that the input space is Rp with its endowed metric ∥•∥2 as a (dis)similarity measure, it follows that selecting hypothesis θ becomes intrinsically ti

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Halpern, Yonatan. "Semi-Supervised Learning for Electronic Phenotyping in Support of Precision Medicine." Thesis, New York University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10192124.

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Medical informatics plays an important role in precision medicine, delivering the right information to the right person, at the right time. With the introduction and widespread adoption of electronic medical records, in the United States and world-wide, there is now a tremendous amount of health data available for analysis. Electronic record phenotyping refers to the task of determining, from an electronic medical record entry, a concise descriptor of the patient, comprising of their medical history, current problems, presentation, etc. In inferring such a phenotype descriptor fro

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Taylor, Farrell R. "Evaluation of Supervised Machine Learning for Classifying Video Traffic." NSUWorks, 2016. http://nsuworks.nova.edu/gscis_etd/972.

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Operational deployment of machine learning based classifiers in real-world networks has become an important area of research to support automated real-time quality of service decisions by Internet service providers (ISPs) and more generally, network administrators. As the Internet has evolved, multimedia applications, such as voice over Internet protocol (VoIP), gaming, and video streaming, have become commonplace. These traffic types are sensitive to network perturbations, e.g. jitter and delay. Automated quality of service (QoS) capabilities offer a degree of relief by prioritizing network t

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Coursey, Kino High. "An Approach Towards Self-Supervised Classification Using Cyc." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5470/.

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Due to the long duration required to perform manual knowledge entry by human knowledge engineers it is desirable to find methods to automatically acquire knowledge about the world by accessing online information. In this work I examine using the Cyc ontology to guide the creation of Naïve Bayes classifiers to provide knowledge about items described in Wikipedia articles. Given an initial set of Wikipedia articles the system uses the ontology to create positive and negative training sets for the classifiers in each category. The order in which classifiers are generated and used to test articles

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Livi, Federico. "Supervised Learning with Graph Structured Data for Transprecision Computing." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19714/.

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Nell'era dell'Internet of things, dei Big Data e dell'industria 4.0, la crescente richiesta di risorse e strumenti atti ad elaborare la grande quantità di dati e di informazioni disponibili in ogni momento, ha posto l'attenzione su problemi oramai non più trascurabili inerenti al consumo di energia e ai costi che ne derivano. Si tratta del cosiddetto powerwall, ovvero della difficoltà fisica dei macchinari di sostenere il consumo di potenza necessario per il processamento di moli di dati sempre più grandi e per l'esecuzione di task sempre più sofisticati. Tra le nuove tecniche che si sono affe

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Rossi, Alex. "Self-supervised information retrieval: a novel approach based on Deep Metric Learning and Neural Language Models." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Most of the existing open-source search engines, utilize keyword or tf-idf based techniques to find relevant documents and web pages relative to an input query. Although these methods, with the help of a page rank or knowledge graphs, proved to be effective in some cases, they often fail to retrieve relevant instances for more complicated queries that would require a semantic understanding to be exploited. In this Thesis, a self-supervised information retrieval system based on transformers is employed to build a semantic search engine over the library of Gruppo Maggioli company. Semantic sear

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Stroulia, Eleni. "Failure-driven learning as model-based self-redesign." Diss., Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/8291.

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Libri sul tema "Self-supervised learning (artificial intelligence)"

Graves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Springer Berlin Heidelberg, 2012.

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Kanerva, Pentti. The organization of an autonomous learning system. Research Institute for Advanced Computer Science, NASA Ames Research Center, 1988.

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He, Haibo. Self-adaptive systems for machine intelligence. Wiley-Interscience, 2011.

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Ekici, Berk. Towards self-sufficient high-rises: Performance optimisation using artificial intelligence. BK Books, 2022.

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Najim, K. Learning automata: Theory and applications. Pergamon, 1994.

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Wang, Huaiqing. Manufacturing intelligence for industrial engineering: Methods for system self-organization, learning, and adaptation. Engineering Science Reference, 2010.

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Zhou, Zude. Manufacturing intelligence for industrial engineering: Methods for system self-organization, learning, and adaptation. Engineering Science Reference, 2010.

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Zhou, Zude. Manufacturing intelligence for industrial engineering: Methods for system self-organization, learning, and adaptation. Engineering Science Reference, 2010.

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Zhou, Zude. Manufacturing intelligence for industrial engineering: Methods for system self-organization, learning, and adaptation. Engineering Science Reference, 2010.

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Klimenko, A. V. Osnovy estestvennogo intellekta: Rekurrentnai͡a︡ teorii͡a︡ samoorganizat͡s︡ii : versii͡a︡ 3. Izd-vo Rostovskogo universiteta, 1994.

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Capitoli di libri sul tema "Self-supervised learning (artificial intelligence)"

Kim, Haesik. "Supervised Learning." In Artificial Intelligence for 6G. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95041-5_4.

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Roy, Radhika Ranjan. "Supervised Machine Learning." In Networked Artificial Intelligence. Auerbach Publications, 2024. http://dx.doi.org/10.1201/9781003499466-9.

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Talukdar, Jyotismita, Thipendra P. Singh, and Basanta Barman. "Supervised Learning." In Artificial Intelligence in Healthcare Industry. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3157-6_4.

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Narayan, Arasu. "Supervised Learning." In Artificial Intelligence and Biological Sciences. CRC Press, 2025. https://doi.org/10.1201/9781003492726-7.

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Liu, Dongxin, and Tarek Abdelzaher. "Self-Supervised Learning from Unlabeled IoT Data." In Artificial Intelligence for Edge Computing. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-40787-1_2.

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Abro, Waheed Ahmed, Hanane Kteich, and Zied Bouraoui. "Self-supervised Segment Contrastive Learning for Medical Document Representation." In Artificial Intelligence in Medicine. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-66538-7_31.

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Long, Jiefeng, Chun Li, and Lin Shang. "Few-Shot Crowd Counting via Self-supervised Learning." In PRICAI 2021: Trends in Artificial Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89370-5_28.

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Ye, Linwei, and Zhenhua Wang. "Self-supervised Meta Auxiliary Learning for Actor and Action Video Segmentation from Natural Language." In Artificial Intelligence. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8850-1_26.

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Siriborvornratanakul, Thitirat. "Reducing Human Annotation Effort Using Self-supervised Learning for Image Segmentation." In Artificial Intelligence in HCI. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60606-9_26.

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Mutschler, Christopher, Georgios Kontes, Sebastian Rietsch, and Sebastian Rietsch. "Learning from Experience." In Unlocking Artificial Intelligence. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64832-8_3.

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AbstractReinforcement Learning (RL) is one of the branches of Machine Learning (ML) that aims to learn from the interaction with an environment. In contrast to approaches such as supervised or unsupervised learning, where data samples usually are assigned to a ground truth label (supervised learning) or where they follow some stationary distribution (unsupervised learning), in RL, the agent is learning in direct interaction with the environment. This also defines what data is being collected as a result of which actions are being executed. The agent is hence learning from experience. While mor

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Atti di convegni sul tema "Self-supervised learning (artificial intelligence)"

Wang, Depei, Cheng Luo, Wenyi Sun, Shulan Wang, and Hongwei Liu. "Text Classification Method Based on Self-Supervised Contrastive Learning." In 2024 8th Asian Conference on Artificial Intelligence Technology (ACAIT). IEEE, 2024. https://doi.org/10.1109/acait63902.2024.11022131.

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Huang, Luzhe, Hanlong Chen, Tairan Liu, and Aydogan Ozcan. "GedankenNet: self-supervised learning of holographic imaging enabled by physics consistency." In Emerging Topics in Artificial Intelligence (ETAI) 2024, edited by Giovanni Volpe, Joana B. Pereira, Daniel Brunner, and Aydogan Ozcan. SPIE, 2024. http://dx.doi.org/10.1117/12.3027298.

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Zhao, Zhiyu, Yongli Wang, and Dongmei Liu. "Self-Supervised Learning Recommendation with Enhanced Long-Tail Nodes." In 2024 5th International Conference on Artificial Intelligence and Computer Engineering (ICAICE). IEEE, 2024. https://doi.org/10.1109/icaice63571.2024.10864218.

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Lin, Chia-Yu, and Yi-Zhen Chen. "Inpainting-bBased Anomaly Detection System with Self-Supervised Learning." In 2024 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT). IEEE, 2024. http://dx.doi.org/10.1109/iaict62357.2024.10617749.

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Wang, Jiaju, Ming Li, Wanwan Cao, Longyue Li, Tiexun Zhang, and Jin Lv. "Substation defect detection model based on self-supervised learning." In 2024 IEEE 4th International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). IEEE, 2024. https://doi.org/10.1109/iciba62489.2024.10867943.

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Zuo, Xumin, Jiayi Wu, Xiaoxing Yang, Heng Zhao, and Bingding Huang. "IProbeTrans: A Long-Term Series Forecasting Method Based on Self-Supervised Learning." In 2024 8th Asian Conference on Artificial Intelligence Technology (ACAIT). IEEE, 2024. https://doi.org/10.1109/acait63902.2024.11021826.

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Shen, Wuqiang, Tao Dai, Zhaopeng Chen, and Jiaxiao Meng. "CluSAD: Self-Supervised Learning-Based Anomaly Detection for Industrial Control Systems." In 2024 5th International Conference on Electronic Communication and Artificial Intelligence (ICECAI). IEEE, 2024. http://dx.doi.org/10.1109/icecai62591.2024.10675256.

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Liu, Lingjun, Jiabao Zhong, Xier Tan, Haoye Jiang, Haoyi Tang, and Zhonghua Xie. "Self-Supervised Image Denoising with Blind-Spot Network and Residual Learning." In 2024 7th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2024. https://doi.org/10.1109/prai62207.2024.10827127.

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Islam, Tafikul, and Yafei Wang. "Beyond Photometric Constraints: Epipolar-Based Self-Supervised Learning for Visual Odometry." In 2024 5th International Conference on Computers and Artificial Intelligence Technology (CAIT). IEEE, 2024. https://doi.org/10.1109/cait64506.2024.10962878.

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Zhou, Zhongliang, and Jiayong Fang. "Self-supervised video representation learning based on foreground and temporal information." In Fourth International Conference on Electronics Technology and Artificial Intelligence (ETAI 2025), edited by Shaohua Luo and Akash Saxena. SPIE, 2025. https://doi.org/10.1117/12.3068403.

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Rapporti di organizzazioni sul tema "Self-supervised learning (artificial intelligence)"

Pasupuleti, Murali Krishna. Automated Smart Contracts: AI-powered Blockchain Technologies for Secure and Intelligent Decentralized Governance. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv425.

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Abstract: Automated smart contracts represent a paradigm shift in decentralized governance by integrating artificial intelligence (AI) with blockchain technologies to enhance security, scalability, and adaptability. Traditional smart contracts, while enabling trustless and automated transactions, often lack the flexibility to adapt to dynamic regulatory frameworks, evolving economic conditions, and real-time security threats. AI-powered smart contracts leverage machine learning, reinforcement learning, and predictive analytics to optimize contract execution, detect fraudulent transactions, and

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Pasupuleti, Murali Krishna. Stochastic Computation for AI: Bayesian Inference, Uncertainty, and Optimization. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv325.

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Abstract: Stochastic computation is a fundamental approach in artificial intelligence (AI) that enables probabilistic reasoning, uncertainty quantification, and robust decision-making in complex environments. This research explores the theoretical foundations, computational techniques, and real-world applications of stochastic methods, focusing on Bayesian inference, Monte Carlo methods, stochastic optimization, and uncertainty-aware AI models. Key topics include probabilistic graphical models, Markov Chain Monte Carlo (MCMC), variational inference, stochastic gradient descent (SGD), and Bayes

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Alexander, Serena, Bo Yang, Owen Hussey, and Derek Hicks. Examining the Externalities of Highway Capacity Expansions in California: An Analysis of Land Use and Land Cover (LULC) Using Remote Sensing Technology. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2251.

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There are over 590,000 bridges dispersed across the roadway network that stretches across the United States alone. Each bridge with a length of 20 feet or greater must be inspected at least once every 24 months, according to the Federal Highway Act (FHWA) of 1968. This research developed an artificial intelligence (AI)-based framework for bridge and road inspection using drones with multiple sensors collecting capabilities. It is not sufficient to conduct inspections of bridges and roads using cameras alone, so the research team utilized an infrared (IR) camera along with a high-resolution opt

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Kulhandjian, Hovannes. AI-Based Bridge and Road Inspection Framework Using Drones. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2226.

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