Letteratura scientifica selezionata sul tema "Self-supervised learning (artificial intelligence)"
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Articoli di riviste sul tema "Self-supervised learning (artificial intelligence)":
Neghawi, Elie, e Yan Liu. "Enhancing Self-Supervised Learning through Explainable Artificial Intelligence Mechanisms: A Computational Analysis". Big Data and Cognitive Computing 8, n. 6 (3 giugno 2024): 58. http://dx.doi.org/10.3390/bdcc8060058.
CHAN, JASON, IRENA KOPRINSKA e JOSIAH POON. "SEMI-SUPERVISED CLASSIFICATION USING BRIDGING". International Journal on Artificial Intelligence Tools 17, n. 03 (giugno 2008): 415–31. http://dx.doi.org/10.1142/s0218213008003972.
Yuya, KOBAYASHI, Masahiro SUZUKI e Yutaka MATSUO. "Scene Interpretation Method using Transformer and Self-supervised Learning". Transactions of the Japanese Society for Artificial Intelligence 37, n. 2 (1 marzo 2022): I—L75_1–17. http://dx.doi.org/10.1527/tjsai.37-2_i-l75.
Hrycej, Tomas. "Supporting supervised learning by self-organization". Neurocomputing 4, n. 1-2 (febbraio 1992): 17–30. http://dx.doi.org/10.1016/0925-2312(92)90040-v.
Wang, Fei, e Changshui Zhang. "Robust self-tuning semi-supervised learning". Neurocomputing 70, n. 16-18 (ottobre 2007): 2931–39. http://dx.doi.org/10.1016/j.neucom.2006.11.004.
Biscione, Valerio, e Jeffrey S. Bowers. "Learning online visual invariances for novel objects via supervised and self-supervised training". Neural Networks 150 (giugno 2022): 222–36. http://dx.doi.org/10.1016/j.neunet.2022.02.017.
Ma, Jun, Yakun Wen e Liming Yang. "Lagrangian supervised and semi-supervised extreme learning machine". Applied Intelligence 49, n. 2 (25 agosto 2018): 303–18. http://dx.doi.org/10.1007/s10489-018-1273-4.
Che, Feihu, Guohua Yang, Dawei Zhang, Jianhua Tao e Tong Liu. "Self-supervised graph representation learning via bootstrapping". Neurocomputing 456 (ottobre 2021): 88–96. http://dx.doi.org/10.1016/j.neucom.2021.03.123.
Gu, Nannan, Pengying Fan, Mingyu Fan e Di Wang. "Structure regularized self-paced learning for robust semi-supervised pattern classification". Neural Computing and Applications 31, n. 10 (19 aprile 2018): 6559–74. http://dx.doi.org/10.1007/s00521-018-3478-1.
Saravana Kumar, N. M. "IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE IN IMPARTING EDUCATION AND EVALUATING STUDENT PERFORMANCE". Journal of Artificial Intelligence and Capsule Networks 01, n. 01 (2 settembre 2019): 1–9. http://dx.doi.org/10.36548/jaicn.2019.1.001.
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.
In this thesis, we develop approaches to perform self-supervised learning for image and video analysis. Self-supervised representation learning allows to pretrain neural networks to learn general concepts without labels before specializing in downstream tasks faster and with few annotations. We present three contributions to self-supervised image and video representation learning. First, we introduce the theoretical paradigm of soft contrastive learning and its practical implementation called Similarity Contrastive Estimation (SCE) connecting contrastive and relational learning for image representation. Second, SCE is extended to global temporal video representation learning. Lastly, we propose COMEDIAN a pipeline for local-temporal video representation learning for transformers. These contributions achieved state-of-the-art results on multiple benchmarks and led to several academic and technical published contributions
Nett, Ryan. "Dataset and Evaluation of Self-Supervised Learning for Panoramic Depth Estimation". DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2234.
Stanescu, Ana. "Semi-supervised learning for biological sequence classification". Diss., Kansas State University, 2015. http://hdl.handle.net/2097/35810.
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. The process of labeling data can be expensive and time consuming, as it requires domain knowledge and expert involvement. Semi-supervised learning approaches that can make use of unlabeled data, in addition to small amounts of labeled data, can help reduce the costs associated with labeling. In this context, we focus on semi-supervised learning approaches for biological sequence classification. Although an attractive concept, semi-supervised learning does not invariably work as intended. Since the assumptions made by learning algorithms cannot be easily verified without considerable domain knowledge or data exploration, semi-supervised learning is not always "safe" to use. Advantageous utilization of the unlabeled data is problem dependent, and more research is needed to identify algorithms that can be used to increase the effectiveness of semi-supervised learning, in general, and for bioinformatics problems, in particular. At a high level, we aim to identify semi-supervised algorithms and data representations that can be used to learn effective classifiers for genome annotation tasks such as cassette exon identification, splice site identification, and protein localization. In addition, one specific challenge that we address is the "data imbalance" problem, which is prevalent in many domains, including bioinformatics. The data imbalance phenomenon arises when one of the classes to be predicted is underrepresented in the data because instances belonging to that class are rare (noteworthy cases) or difficult to obtain. Ironically, minority classes are typically the most important to learn, because they may be associated with special cases, as in the case of splice site prediction. We propose two main techniques to deal with the data imbalance problem, namely a technique based on "dynamic balancing" (augmenting the originally labeled data only with positive instances during the semi-supervised iterations of the algorithms) and another technique based on ensemble approaches. The results show that with limited amounts of labeled data, semisupervised approaches can successfully leverage the unlabeled data, thereby surpassing their completely supervised counterparts. A type of semi-supervised learning, known as "transductive" learning aims to classify the unlabeled data without generalizing to new, previously not encountered instances. Theoretically, this aspect makes transductive learning particularly suitable for the task of genome annotation, in which an entirely sequenced genome is typically available, sometimes accompanied by limited annotation. We study and evaluate various transductive approaches (such as transductive support vector machines and graph based approaches) and sequence representations for the problems of cassette exon identification. The results obtained demonstrate the effectiveness of transductive algorithms in sequence annotation tasks.
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.
Dans cette thèse, je propose deux algorithmes pour l'apprentissage de la métrique dX; le premier pour l'apprentissage supervisé, et le deuxième pour l'apprentissage non-supervisé, ainsi que pour l'apprentissage supervisé et semi-supervisé. En particulier, je propose des algorithmes qui prennent en considération la structure et la géométrie de X d'une part, et les caractéristiques des ensembles de données du monde réel d'autre part. Cependant, si on cherche également la réduction de dimension, donc sous certaines hypothèses légères sur la topologie de X, et en même temps basé sur des informations disponibles a priori, on peut apprendre une intégration de X dans un espace Euclidien de petite dimension Rp0 p0 << p, où la distance Euclidienne révèle mieux les ressemblances entre les éléments de X et leurs groupements (clusters). Alors, comme un sous-produit, on obtient simultanément une réduction de dimension et un apprentissage métrique. Pour l'apprentissage supervisé, je propose PARDA, ou Pareto discriminant analysis, pour la discriminante réduction linéaire de dimension. PARDA est basé sur le mécanisme d'optimisation à multi-objectifs; optimisant simultanément plusieurs fonctions objectives, éventuellement des fonctions contradictoires. Cela permet à PARDA de s'adapter à la topologie de classe dans un espace dimensionnel plus petit, et naturellement gère le problème de masquage de classe associé au discriminant Fisher dans le cadre d'analyse de problèmes à multi-classes. En conséquence, PARDA permet des meilleurs résultats de classification par rapport aux techniques modernes de réduction discriminante de dimension. Pour l'apprentissage non-supervisés, je propose un cadre algorithmique, noté par ??, qui encapsule les algorithmes spectraux d'apprentissage formant an algorithme d'apprentissage de métrique. Le cadre ?? capture la structure locale et la densité locale d'information de chaque point dans un ensemble de données, et donc il porte toutes les informations sur la densité d'échantillon différente dans l'espace d'entrée. La structure de ?? induit deux métriques de distance pour ses éléments: la métrique Bhattacharyya-Riemann dBR et la métrique Jeffreys-Riemann dJR. Les deux mesures réorganisent la proximité entre les points de X basé sur la structure locale et la densité autour de chaque point. En conséquence, lorsqu'on combine l'espace métrique (??, dBR) ou (??, dJR) avec les algorithmes de "spectral clustering" et "Euclidean embedding", ils donnent des améliorations significatives dans les précisions de regroupement et les taux d'erreur pour une grande variété de tâches de clustering et de classification.
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.
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 from the record, a computer, in a sense, "understands'' the relevant parts of the record. These phenotypes can then be used in downstream applications such as cohort selection for retrospective studies, real-time clinical decision support, contextual displays, intelligent search, and precise alerting mechanisms.
We are faced with three main challenges:
First, the unstructured and incomplete nature of the data recorded in the electronic medical records requires special attention. Relevant information can be missing or written in an obscure way that the computer does not understand.
Second, the scale of the data makes it important to develop efficient methods at all steps of the machine learning pipeline, including data collection and labeling, model learning and inference.
Third, large parts of medicine are well understood by health professionals. How do we combine the expert knowledge of specialists with the statistical insights from the electronic medical record?
Probabilistic graphical models such as Bayesian networks provide a useful abstraction for quantifying uncertainty and describing complex dependencies in data. Although significant progress has been made over the last decade on approximate inference algorithms and structure learning from complete data, learning models with incomplete data remains one of machine learning’s most challenging problems. How can we model the effects of latent variables that are not directly observed?
The first part of the thesis presents two different structural conditions under which learning with latent variables is computationally tractable. The first is the "anchored'' condition, where every latent variable has at least one child that is not shared by any other parent. The second is the "singly-coupled'' condition, where every latent variable is connected to at least three children that satisfy conditional independence (possibly after transforming the data).
Variables that satisfy these conditions can be specified by an expert without requiring that the entire structure or its parameters be specified, allowing for effective use of human expertise and making room for statistical learning to do some of the heavy lifting. For both the anchored and singly-coupled conditions, practical algorithms are presented.
The second part of the thesis describes real-life applications using the anchored condition for electronic phenotyping. A human-in-the-loop learning system and a functioning emergency informatics system for real-time extraction of important clinical variables are described and evaluated.
The algorithms and discussion presented here were developed for the purpose of improving healthcare, but are much more widely applicable, dealing with the very basic questions of identifiability and learning models with latent variables - a problem that lies at the very heart of the natural and social sciences.
Taylor, Farrell R. "Evaluation of Supervised Machine Learning for Classifying Video Traffic". NSUWorks, 2016. http://nsuworks.nova.edu/gscis_etd/972.
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/.
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/.
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.
Stroulia, Eleni. "Failure-driven learning as model-based self-redesign". Diss., Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/8291.
Libri sul tema "Self-supervised learning (artificial intelligence)":
Graves, Alex. Supervised Sequence Labelling with Recurrent Neural Networks. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Kanerva, Pentti. The organization of an autonomous learning system. Moffett Field, CA: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1988.
Ekici, Berk. Towards self-sufficient high-rises: Performance optimisation using artificial intelligence. Delft: BK Books, 2022.
He, Haibo. Self-adaptive systems for machine intelligence. Hoboken, N.J: Wiley-Interscience, 2011.
Najim, K. Learning automata: Theory and applications. Oxford, OX, U.K: Pergamon, 1994.
Wang, Huaiqing. Manufacturing intelligence for industrial engineering: Methods for system self-organization, learning, and adaptation. Hershey PA: Engineering Science Reference, 2010.
Zhou, Zude. Manufacturing intelligence for industrial engineering: Methods for system self-organization, learning, and adaptation. Hershey PA: Engineering Science Reference, 2010.
Zhou, Zude. Manufacturing intelligence for industrial engineering: Methods for system self-organization, learning, and adaptation. Hershey PA: Engineering Science Reference, 2010.
Zhou, Zude. Manufacturing intelligence for industrial engineering: Methods for system self-organization, learning, and adaptation. Hershey, PA: Engineering Science Reference, 2010.
Klimenko, A. V. Osnovy estestvennogo intellekta: Rekurrentnai͡a︡ teorii͡a︡ samoorganizat͡s︡ii : versii͡a︡ 3. Rostov-na-Donu: Izd-vo Rostovskogo universiteta, 1994.
Capitoli di libri sul tema "Self-supervised learning (artificial intelligence)":
Kim, Haesik. "Supervised Learning". In Artificial Intelligence for 6G, 87–182. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95041-5_4.
Talukdar, Jyotismita, Thipendra P. Singh e Basanta Barman. "Supervised Learning". In Artificial Intelligence in Healthcare Industry, 51–86. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3157-6_4.
Liu, Dongxin, e Tarek Abdelzaher. "Self-Supervised Learning from Unlabeled IoT Data". In Artificial Intelligence for Edge Computing, 27–110. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-40787-1_2.
Ye, Linwei, e Zhenhua Wang. "Self-supervised Meta Auxiliary Learning for Actor and Action Video Segmentation from Natural Language". In Artificial Intelligence, 317–28. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-8850-1_26.
Long, Jiefeng, Chun Li e Lin Shang. "Few-Shot Crowd Counting via Self-supervised Learning". In PRICAI 2021: Trends in Artificial Intelligence, 379–90. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89370-5_28.
Siriborvornratanakul, Thitirat. "Reducing Human Annotation Effort Using Self-supervised Learning for Image Segmentation". In Artificial Intelligence in HCI, 436–45. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-60606-9_26.
Slama, Dirk. "Artificial Intelligence 101". In The Digital Playbook, 11–17. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-88221-1_2.
Yang, Yu, Fang Wan, Qixiang Ye e Xiangyang Ji. "Weakly Supervised Learning of Instance Segmentation with Confidence Feedback". In Artificial Intelligence, 392–403. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20497-5_32.
Chen, Zhiyuan, e Bing Liu. "Lifelong Supervised Learning". In Synthesis Lectures on Artificial Intelligence and Machine Learning, 27–51. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-031-01575-5_3.
Mosalam, Khalid M., e Yuqing Gao. "Semi-Supervised Learning". In Artificial Intelligence in Vision-Based Structural Health Monitoring, 279–305. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-52407-3_10.
Atti di convegni sul tema "Self-supervised learning (artificial intelligence)":
An, Yuexuan, Hui Xue, Xingyu Zhao e Lu Zhang. "Conditional Self-Supervised Learning for Few-Shot Classification". In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/295.
Liang, Yudong, Bin Wang, Wangmeng Zuo, Jiaying Liu e Wenqi Ren. "Self-supervised Learning and Adaptation for Single Image Dehazing". In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/159.
Shen, Jiahao. "Self-supervised boundary offline reinforcement learning". In International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), a cura di Harris Wu e Haiwu Li. SPIE, 2024. http://dx.doi.org/10.1117/12.3026355.
Ismail-Fawaz, Ali, Maxime Devanne, Jonathan Weber e Germain Forestier. "Enhancing Time Series Classification with Self-Supervised Learning". In 15th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011611300003393.
Tang, Yixin, Hua Cheng, Yiquan Fang e Yiming Pan. "In-Batch Negatives' Enhanced Self-Supervised Learning". In 2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2022. http://dx.doi.org/10.1109/ictai56018.2022.00031.
Wicaksono, R. Satrio Hariomurti, Ali Akbar Septiandri e Ade Jamal. "Human Embryo Classification Using Self-Supervised Learning". In 2021 2nd International Conference on Artificial Intelligence and Data Sciences (AiDAS). IEEE, 2021. http://dx.doi.org/10.1109/aidas53897.2021.9574328.
Khan, Adnan, Sarah AlBarri e Muhammad Arslan Manzoor. "Contrastive Self-Supervised Learning: A Survey on Different Architectures". In 2022 2nd International Conference on Artificial Intelligence (ICAI). IEEE, 2022. http://dx.doi.org/10.1109/icai55435.2022.9773725.
Basaj, Dominika, Witold Oleszkiewicz, Igor Sieradzki, Michał Górszczak, Barbara Rychalska, Tomasz Trzcinski e Bartosz Zieliński. "Explaining Self-Supervised Image Representations with Visual Probing". In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/82.
Bhattacharjee, Amrita, Mansooreh Karami e Huan Liu. "Text Transformations in Contrastive Self-Supervised Learning: A Review". In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/757.
Yang, XiaoYu, e CaiFeng Zhou. "Self-supervised learning-based waste classification model". In 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC2023), a cura di Dimitrios A. Karras e Simon X. Yang. SPIE, 2023. http://dx.doi.org/10.1117/12.2684730.
Rapporti di organizzazioni sul tema "Self-supervised learning (artificial intelligence)":
Alexander, Serena, Bo Yang, Owen Hussey e 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, novembre 2023. http://dx.doi.org/10.31979/mti.2023.2251.
Kulhandjian, Hovannes. AI-Based Bridge and Road Inspection Framework Using Drones. Mineta Transportation Institute, novembre 2023. http://dx.doi.org/10.31979/mti.2023.2226.