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Articles de revues sur le sujet « Self-supervised learning (artificial intelligence) »

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Auteur : Grafiati
Publié le 7 juillet 2024

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1

Neghawi, Elie, et Yan Liu. « Enhancing Self-Supervised Learning through Explainable Artificial Intelligence Mechanisms : A Computational Analysis ». Big Data and Cognitive Computing 8, no 6 (3 juin 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 (XAI) methodology that is capable of systematically integrating and enhancing state-of-the-art algorithms. Through UMAC, we identify key computational mechanisms and craft a unified framework for self-supervised learning evaluation. Leveraging UMAC, we integrate an XAI methodology to enhance transparency and interpretability. Our systematic approach yields a 17.12% increase in improvement in training time complexity and a 13.1% boost in improvement in testing time complexity. Notably, improvements are observed in augmentation, encoder architecture, and auxiliary components within the network classifier. These findings underscore the importance of structured computational processes in enhancing model efficiency and fortifying algorithmic transparency in self-supervised learning, paving the way for more interpretable and efficient AI models.
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CHAN, JASON, IRENA KOPRINSKA et JOSIAH POON. « SEMI-SUPERVISED CLASSIFICATION USING BRIDGING ». International Journal on Artificial Intelligence Tools 17, no 03 (juin 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 two semi-supervised algorithms, self-learning and co-training, improves with the use of our new bridging algorithm in comparison to using the standard classifier, JRipper. We propose a similarity metric for short texts and also study the performance of self-learning with a number of instance selection heuristics.
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Yuya, KOBAYASHI, Masahiro SUZUKI et Yutaka MATSUO. « Scene Interpretation Method using Transformer and Self-supervised Learning ». Transactions of the Japanese Society for Artificial Intelligence 37, no 2 (1 mars 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 (février 1992) : 17–30. http://dx.doi.org/10.1016/0925-2312(92)90040-v.

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

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

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Ma, Jun, Yakun Wen et Liming Yang. « Lagrangian supervised and semi-supervised extreme learning machine ». Applied Intelligence 49, no 2 (25 août 2018) : 303–18. http://dx.doi.org/10.1007/s10489-018-1273-4.

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Che, Feihu, Guohua Yang, Dawei Zhang, Jianhua Tao et Tong Liu. « Self-supervised graph representation learning via bootstrapping ». Neurocomputing 456 (octobre 2021) : 88–96. http://dx.doi.org/10.1016/j.neucom.2021.03.123.

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Gu, Nannan, Pengying Fan, Mingyu Fan et Di Wang. « Structure regularized self-paced learning for robust semi-supervised pattern classification ». Neural Computing and Applications 31, no 10 (19 avril 2018) : 6559–74. http://dx.doi.org/10.1007/s00521-018-3478-1.

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Saravana Kumar, N. M. « IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE IN IMPARTING EDUCATION AND EVALUATING STUDENT PERFORMANCE ». Journal of Artificial Intelligence and Capsule Networks 01, no 01 (2 septembre 2019) : 1–9. http://dx.doi.org/10.36548/jaicn.2019.1.001.

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Simulation of human intelligence process is made possible with the help of artificial intelligence. The learning, reasoning and self-correction properties are made possible in computer systems. Along with AI, other technologies are combined effectively in order to create remarkable applications. We apply the changing role of AI and its techniques in new educational paradigms to create a personalised teaching-learning environment. Features like recognition, pattern matching, decision making, reasoning, problem solving and so on are applied along with knowledge based system and supervised machine learning for a complete learning and assessment process.
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Wei, Chen, Yiping Tang, Chuang Niu Chuang Niu, Haihong Hu, Yue Wang et Jimin Liang. « Self-Supervised Representation Learning for Evolutionary Neural Architecture Search ». IEEE Computational Intelligence Magazine 16, no 3 (août 2021) : 33–49. http://dx.doi.org/10.1109/mci.2021.3084415.

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Xi, Liang, Zichao Yun, Han Liu, Ruidong Wang, Xunhua Huang et Haoyi Fan. « Semi-supervised Time Series Classification Model with Self-supervised Learning ». Engineering Applications of Artificial Intelligence 116 (novembre 2022) : 105331. http://dx.doi.org/10.1016/j.engappai.2022.105331.

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Serey, Joel, Luis Quezada, Miguel Alfaro, Guillermo Fuertes, Manuel Vargas, Rodrigo Ternero, Jorge Sabattin, Claudia Duran et Sebastian Gutierrez. « Artificial Intelligence Methodologies for Data Management ». Symmetry 13, no 11 (29 octobre 2021) : 2040. http://dx.doi.org/10.3390/sym13112040.

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This study analyses the main challenges, trends, technological approaches, and artificial intelligence methods developed by new researchers and professionals in the field of machine learning, with an emphasis on the most outstanding and relevant works to date. This literature review evaluates the main methodological contributions of artificial intelligence through machine learning. The methodology used to study the documents was content analysis; the basic terminology of the study corresponds to machine learning, artificial intelligence, and big data between the years 2017 and 2021. For this study, we selected 181 references, of which 120 are part of the literature review. The conceptual framework includes 12 categories, four groups, and eight subgroups. The study of data management using AI methodologies presents symmetry in the four machine learning groups: supervised learning, unsupervised learning, semi-supervised learning, and reinforced learning. Furthermore, the artificial intelligence methods with more symmetry in all groups are artificial neural networks, Support Vector Machines, K-means, and Bayesian Methods. Finally, five research avenues are presented to improve the prediction of machine learning.
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Kozhuharov, Mihail. « Artificial Intelligence : Basic Concepts ». Педагогически форум 11, no 4 (2023) : 3–24. http://dx.doi.org/10.15547/pf.2023.023.

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This study presents basic concepts embedded in the scientific field of artificial intelligence with an emphasis on key aspects and methods of application. Concepts such as machine learning with its varieties such as supervised, unsupervised machine learning and reinforcement learning are explored, emphasizing the importance of machine learning, which allows systems to adapt and improve without explicit programming. The specifics of deep machine learning are discussed, with an emphasis on their capacity to process complex data and extract patterns. The article also examines some of the areas that make up artificial intelligence, such as natural language processing, large language models, computer vision, generative artificial intelligence, and others. By discussing these key concepts, the article aims to provide a basic understanding of the application and potential of artificial intelligence in various spheres of society.
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Takama, Yasufumi. « Web Intelligence and Artificial Intelligence ». Journal of Advanced Computational Intelligence and Intelligent Informatics 21, no 1 (20 janvier 2017) : 25–30. http://dx.doi.org/10.20965/jaciii.2017.p0025.

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This paper briefly summarizes the progress of artificial intelligence (AI) and web intelligence (WI) in the last two decades. The reason why we mention AI and WI together is because those have strong relationship with each other. This paper first summarizes the history of AI, and then gives brief description of supervised learning, which I think has played a major role in AI in the last two decades. As most history of WI is in the target decades, this paper first briefly describes major WI topics, and then gives more detailed description about information recommendation, which I think one of more successful and necessary technologies in practical use.
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Ledziński, Łukasz, et Grzegorz Grześk. « Artificial Intelligence Technologies in Cardiology ». Journal of Cardiovascular Development and Disease 10, no 5 (6 mai 2023) : 202. http://dx.doi.org/10.3390/jcdd10050202.

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As the world produces exabytes of data, there is a growing need to find new methods that are more suitable for dealing with complex datasets. Artificial intelligence (AI) has significant potential to impact the healthcare industry, which is already on the road to change with the digital transformation of vast quantities of information. The implementation of AI has already achieved success in the domains of molecular chemistry and drug discoveries. The reduction in costs and in the time needed for experiments to predict the pharmacological activities of new molecules is a milestone in science. These successful applications of AI algorithms provide hope for a revolution in healthcare systems. A significant part of artificial intelligence is machine learning (ML), of which there are three main types—supervised learning, unsupervised learning, and reinforcement learning. In this review, the full scope of the AI workflow is presented, with explanations of the most-often-used ML algorithms and descriptions of performance metrics for both regression and classification. A brief introduction to explainable artificial intelligence (XAI) is provided, with examples of technologies that have developed for XAI. We review important AI implementations in cardiology for supervised, unsupervised, and reinforcement learning and natural language processing, emphasizing the used algorithm. Finally, we discuss the need to establish legal, ethical, and methodical requirements for the deployment of AI models in medicine.
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Yamauchi, K., M. Oota et N. Ishii. « A self-supervised learning system for pattern recognition by sensory integration ». Neural Networks 12, no 10 (décembre 1999) : 1347–58. http://dx.doi.org/10.1016/s0893-6080(99)00064-7.

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Dushkin, R. V. « Semantic Supervised Training for General Artificial Cognitive Agents ». Siberian Journal of Philosophy 19, no 2 (21 octobre 2021) : 51–64. http://dx.doi.org/10.25205/2541-7517-2021-19-2-51-64.

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The article describes the author's approach to the construction of general-level artificial cognitive agents based on the so-called "semantic supervised learning", within which, in accordance with the hybrid paradigm of artificial intelligence, both machine learning methods and methods of the symbolic ap­ proach and knowledge-based systems are used ("good old-fashioned artificial intelligence"). А descrip­ tion of current proЬlems with understanding of the general meaning and context of situations in which narrow AI agents are found is presented. The definition of semantic supervised learning is given and its relationship with other machine learning methods is described. In addition, а thought experiment is presented, which shows the essence and meaning of supervised semantic learning.
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Florence, Peter, Lucas Manuelli et Russ Tedrake. « Self-Supervised Correspondence in Visuomotor Policy Learning ». IEEE Robotics and Automation Letters 5, no 2 (avril 2020) : 492–99. http://dx.doi.org/10.1109/lra.2019.2956365.

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Pal, S. K., A. Pathak et C. Basu. « Dynamic guard zone for self-supervised learning ». Pattern Recognition Letters 7, no 3 (mars 1988) : 135–44. http://dx.doi.org/10.1016/0167-8655(88)90056-6.

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Soni, Kuldeep, Nidhi Pateria et Gulafsha Anjum. « Artificial Intelligence and Machine Learning in Sport Medicines ». International Journal of Innovative Research in Computer and Communication Engineering 12, Special Is (25 mars 2024) : 69–73. http://dx.doi.org/10.15680/ijircce.2024.1203511.

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Orthopedic sports medicine is starting to feel the impact of machine learning (ML), which is transforming healthcare procedures. Orthopedic sports medicine professionals can now analyze enormous volumes of patient data to obtain insights that were previously unreachable through traditional approaches by utilizing machine learning algorithms .Large datasets can be tested more easily with machine learning to find complex saga between input and output variables. These correlations may be more complicated than what can be achieved with conventional statistical techniques, allowing for precise output predictions. For healthcare data, supervised learning is the most popular machine learning technique. Supervised learning algorithms have been applied in recent research to forecast individual patient outcomes after surgery, such as hip arthroscopy. These algorithms have the ability to improve postoperative care, optimize surgical procedures, and improve preoperative planning by utilizing extent volumes of patient data, which will ultimately improve patient outcomes in orthopedic surgery.
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Li, Li, Kaiyi Zhao, Sicong Li, Ruizhi Sun et Saihua Cai. « Extreme Learning Machine for Supervised Classification with Self-paced Learning ». Neural Processing Letters 52, no 3 (14 juin 2020) : 1723–44. http://dx.doi.org/10.1007/s11063-020-10286-9.

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Miranda, Enrique, et Jordi Suñé. « Memristors for Neuromorphic Circuits and Artificial Intelligence Applications ». Materials 13, no 4 (20 février 2020) : 938. http://dx.doi.org/10.3390/ma13040938.

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Artificial Intelligence has found many applications in the last decade due to increased computing power. Artificial Neural Networks are inspired in the brain structure and consist in the interconnection of artificial neurons through artificial synapses in the so-called Deep Neural Networks (DNNs). Training these systems requires huge amounts of data and, after the network is trained, it can recognize unforeseen data and provide useful information. As far as the training is concerned, we can distinguish between supervised and unsupervised learning. The former requires labelled data and is based on the iterative minimization of the output error using the stochastic gradient descent method followed by the recalculation of the strength of the synaptic connections (weights) with the backpropagation algorithm. On the other hand, unsupervised learning does not require data labeling and it is not based on explicit output error minimization. Conventional ANNs can function with supervised learning algorithms (perceptrons, multi-layer perceptrons, convolutional networks, etc.) but also with unsupervised learning rules (Kohonen networks, self-organizing maps, etc.). Besides, another type of neural networks are the so-called Spiking Neural Networks (SNNs) in which learning takes place through the superposition of voltage spikes launched by the neurons. Their behavior is much closer to the brain functioning mechanisms they can be used with supervised and unsupervised learning rules. Since learning and inference is based on short voltage spikes, energy efficiency improves substantially. Up to this moment, all these ANNs (spiking and conventional) have been implemented as software tools running on conventional computing units based on the von Neumann architecture. However, this approach reaches important limits due to the required computing power, physical size and energy consumption. This is particularly true for applications at the edge of the internet. Thus, there is an increasing interest in developing AI tools directly implemented in hardware for this type of applications. The first hardware demonstrations have been based on Complementary Metal-Oxide-Semiconductor (CMOS) circuits and specific communication protocols. However, to further increase training speed andenergy efficiency while reducing the system size, the combination of CMOS neuron circuits with memristor synapses is now being explored. It has also been pointed out that the short time non-volatility of some memristors may even allow fabricating purely memristive ANNs. The memristor is a new device (first demonstrated in solid-state in 2008) which behaves as a resistor with memory and which has been shown to have potentiation and depression properties similar to those of biological synapses. In this Special Issue, we explore the state of the art of neuromorphic circuits implementing neural networks with memristors for AI applications.
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Mody, Rohit, Debabrata Dash et Deepanshu Mody. « Artificial intelligence in coronary physiology : where do we stand ? » Journal of Transcatheter Interventions 30 (28 octobre 2022) : 1–9. http://dx.doi.org/10.31160/jotci202230a20220009.

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The use of invasive coronary physiology to select individuals for coronary revascularization has been established in current guidelines for the management of stable coronary artery disease. Compared to angiography alone, coronary physiology has been proven to improve clinical outcomes and cost-effectiveness in the revascularization process. Randomized controlled trials, however, have questioned the efficacy of ischemia testing in selecting individuals for revascularization. After an angiographically successful percutaneous coronary intervention, 20 to 40% of patients experienced persistent or recurrent angina. Artificial intelligence is defined as the usage of various algorithms and computational concepts to perform the complex tasks in an efficient manner. It can be classified into two types: unsupervised and supervised approaches. Supervised learning is majorly used for the regression and classification tasks, and in this optimized mapping between output variables and paired input is carried out to perform the tasks. In contrast to this, unsupervised learning works in the different manner. In unsupervised learning, output variables data is not available and further clusters and relations between input data are found out by using the various algorithms. To acquire more abstract representation of data, deep learning technology, which uses the multilayer neural networks, dominates the artificial learning nowadays.
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Okadome, Yuya, Kenshiro Ata, Hiroshi Ishiguro et Yutaka Nakamura. « Self-supervised Learning Method for Behavior Prediction during Dialogue Based on Temporal Consistency ». Transactions of the Japanese Society for Artificial Intelligence 37, no 6 (1 novembre 2022) : B—M43_1–13. http://dx.doi.org/10.1527/tjsai.37-6_b-m43.

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Benavides-Prado, Diana, Yun Sing Koh et Patricia Riddle. « Towards Knowledgeable Supervised Lifelong Learning Systems ». Journal of Artificial Intelligence Research 68 (8 mai 2020) : 159–224. http://dx.doi.org/10.1613/jair.1.11432.

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Learning a sequence of tasks is a long-standing challenge in machine learning. This setting applies to learning systems that observe examples of a range of tasks at different points in time. A learning system should become more knowledgeable as more related tasks are learned. Although the problem of learning sequentially was acknowledged for the first time decades ago, the research in this area has been rather limited. Research in transfer learning, multitask learning, metalearning and deep learning has studied some challenges of these kinds of systems. Recent research in lifelong machine learning and continual learning has revived interest in this problem. We propose Proficiente, a full framework for long-term learning systems. Proficiente relies on knowledge transferred between hypotheses learned with Support Vector Machines. The first component of the framework is focused on transferring forward selectively from a set of existing hypotheses or functions representing knowledge acquired during previous tasks to a new target task. A second component of Proficiente is focused on transferring backward, a novel ability of long-term learning systems that aim to exploit knowledge derived from recent tasks to encourage refinement of existing knowledge. We propose a method that transfers selectively from a task learned recently to existing hypotheses representing previous tasks. The method encourages retention of existing knowledge whilst refining. We analyse the theoretical properties of the proposed framework. Proficiente is accompanied by an agnostic metric that can be used to determine if a long-term learning system is becoming more knowledgeable. We evaluate Proficiente in both synthetic and real-world datasets, and demonstrate scenarios where knowledgeable supervised learning systems can be achieved by means of transfer.
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Okori, Washington, et Joseph Obua. « SUPERVISED LEARNING ALGORITHMS FOR FAMINE PREDICTION ». Applied Artificial Intelligence 25, no 9 (octobre 2011) : 822–35. http://dx.doi.org/10.1080/08839514.2011.611930.

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Poulos, Jason, et Rafael Valle. « Missing Data Imputation for Supervised Learning ». Applied Artificial Intelligence 32, no 2 (19 mars 2018) : 186–96. http://dx.doi.org/10.1080/08839514.2018.1448143.

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Weinlichová, Jana, et Jiří Fejfar. « Usage of self-organizing neural networks in evaluation of consumer behaviour ». Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 58, no 6 (2010) : 625–32. http://dx.doi.org/10.11118/actaun201058060625.

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This article deals with evaluation of consumer data by Artificial Intelligence methods. In methodical part there are described learning algorithms for Kohonen maps on the principle of supervised learning, unsupervised learning and semi-supervised learning. The principles of supervised learning and unsupervised learning are compared. On base of binding conditions of these principles there is pointed out an advantage of semi-supervised learning. Three algorithms are described for the semi-supervised learning: label propagation, self-training and co-training. Especially usage of co-training in Kohonen map learning seems to be promising point of other research. In concrete application of Kohonen neural network on consumer’s expense the unsupervised learning method has been chosen – the self-organization. So the features of data are evaluated by clustering method called Kohonen maps. These input data represents consumer expenses of households in countries of European union and are characterised by 12-dimension vector according to commodity classification. The data are evaluated in several years, so we can see their distribution, similarity or dissimilarity and also their evolution. In the article we discus other usage of this method for this type of data and also comparison of our results with results reached by hierarchical cluster analysis.
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Hashimoto, Daniel A., Elan Witkowski, Lei Gao, Ozanan Meireles et Guy Rosman. « Artificial Intelligence in Anesthesiology ». Anesthesiology 132, no 2 (1 février 2020) : 379–94. http://dx.doi.org/10.1097/aln.0000000000002960.

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Abstract Artificial intelligence has been advancing in fields including anesthesiology. This scoping review of the intersection of artificial intelligence and anesthesia research identified and summarized six themes of applications of artificial intelligence in anesthesiology: (1) depth of anesthesia monitoring, (2) control of anesthesia, (3) event and risk prediction, (4) ultrasound guidance, (5) pain management, and (6) operating room logistics. Based on papers identified in the review, several topics within artificial intelligence were described and summarized: (1) machine learning (including supervised, unsupervised, and reinforcement learning), (2) techniques in artificial intelligence (e.g., classical machine learning, neural networks and deep learning, Bayesian methods), and (3) major applied fields in artificial intelligence. The implications of artificial intelligence for the practicing anesthesiologist are discussed as are its limitations and the role of clinicians in further developing artificial intelligence for use in clinical care. Artificial intelligence has the potential to impact the practice of anesthesiology in aspects ranging from perioperative support to critical care delivery to outpatient pain management.
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Lu, Keyu, Chengyi Zeng et Yonghu Zeng. « Self-supervised learning of monocular depth using quantized networks ». Neurocomputing 488 (juin 2022) : 634–46. http://dx.doi.org/10.1016/j.neucom.2021.11.071.

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Hou, Wenjie, Zheyun Qin, Xiaoming Xi, Xiankai Lu et Yilong Yin. « Learning disentangled representation for self-supervised video object segmentation ». Neurocomputing 481 (avril 2022) : 270–80. http://dx.doi.org/10.1016/j.neucom.2022.01.066.

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Chen, Long, Wen Tang, Tao Ruan Wan et Nigel W. John. « Self-supervised monocular image depth learning and confidence estimation ». Neurocomputing 381 (mars 2020) : 272–81. http://dx.doi.org/10.1016/j.neucom.2019.11.038.

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Aryal, Gopi. « Artificial intelligence in surgical pathology ». Journal of Pathology of Nepal 9, no 1 (2 avril 2019) : I. http://dx.doi.org/10.3126/jpn.v9i1.23444.

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Artificial intelligence (AI) is machine intelligence that mimics human cognitive function. It denotes the intelligence presented by some artificial entities including computers and robots. In supervised learning, a machine is trained with data that contain pairs of inputs and outputs. In unsupervised learning, machines are given data inputs that are not explicitly programmed.1 Machine learning refines a model that predicts outputs using sample inputs (features) and a feedback loop. It relies heavily on extracting or selecting salient features, which is a combination of art and science (“feature engineering”). A subset of feature learning is deep learning, which harnesses neural networks modeled after the biological nervous system of animals. Deep learning discovers the features from the raw data provided during training. Hidden layers in the artificial neural network represent increasingly more complex features in the data. Convolutional neural network is a type of deep learning commonly used for image analysis.
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Xu, Rongge, Ruiyang Hao et Biqing Huang. « Efficient surface defect detection using self-supervised learning strategy and segmentation network ». Advanced Engineering Informatics 52 (avril 2022) : 101566. http://dx.doi.org/10.1016/j.aei.2022.101566.

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Liu, Chicheng, Libin Song, Jiwen Zhang, Ken Chen et Jing Xu. « Self-Supervised Learning for Specified Latent Representation ». IEEE Transactions on Fuzzy Systems 28, no 1 (janvier 2020) : 47–59. http://dx.doi.org/10.1109/tfuzz.2019.2904237.

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PETROVIC, SMILJANA, et SUSAN L. EPSTEIN. « RANDOM SUBSETS SUPPORT LEARNING A MIXTURE OF HEURISTICS ». International Journal on Artificial Intelligence Tools 17, no 03 (juin 2008) : 501–20. http://dx.doi.org/10.1142/s0218213008004023.

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Problem solvers, both human and machine, have at their disposal many heuristics that may support effective search. The efficacy of these heuristics, however, varies with the problem class, and their mutual interactions may not be well understood. The long-term goal of our work is to learn how to select appropriately from among a large body of heuristics, and how to combine them into a mixture that works well on a specific class of problems. The principal result reported here is that randomly chosen subsets of heuristics can improve the identification of an appropriate mixture of heuristics. A self-supervised learner uses this method here to learn to solve constraint satisfaction problems quickly and effectively.
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Xu, Ke, Guoqiang Zhong, Zhaoyang Deng, Kang Zhang et Kaizhu Huang. « Self-supervised generative learning for sequential data prediction ». Applied Intelligence, 20 avril 2023. http://dx.doi.org/10.1007/s10489-023-04578-5.

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« Artificial Intelligence Methodologies for Supervised Learning ». International Journal of Advanced Research in Big Data Management System 3, no 1 (30 mai 2019). http://dx.doi.org/10.21742/ijarbms.2019.3.1.03.

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Li, Simou, Yuxing Mao, Jian Li, Yihang Xu, Jinsen Li, Xueshuo Chen, Siyang Liu et Xianping Zhao. « FedUTN : federated self-supervised learning with updating target network ». Applied Intelligence, 26 août 2022. http://dx.doi.org/10.1007/s10489-022-04070-6.

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Kim, Sangwon, Jimi Lee et Byoung Chul Ko. « SSL-MOT : self-supervised learning based multi-object tracking ». Applied Intelligence, 22 avril 2022. http://dx.doi.org/10.1007/s10489-022-03473-9.

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Wang, Zhipeng, Chunping Hou, Guanghui Yue et Qingyuan Yang. « Dynamic-boosting attention for self-supervised video representation learning ». Applied Intelligence, 1 juillet 2021. http://dx.doi.org/10.1007/s10489-021-02440-0.

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Hafez, Muhammad Burhan, et Stefan Wermter. « Continual Robot Learning Using Self-Supervised Task Inference ». IEEE Transactions on Cognitive and Developmental Systems, 2023, 1. http://dx.doi.org/10.1109/tcds.2023.3315513.

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Wang, Jing, Jun Wu, Caiyan Jia et Zhifei Zhang. « Self-supervised variational autoencoder towards recommendation by nested contrastive learning ». Applied Intelligence, 14 février 2023. http://dx.doi.org/10.1007/s10489-023-04488-6.

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Li, Jinlong, Zequn Jie, Xu Wang, Yu Zhou, Lin Ma et Jianmin Jiang. « Weakly supervised semantic segmentation via self-supervised destruction learning ». Neurocomputing, septembre 2023, 126821. http://dx.doi.org/10.1016/j.neucom.2023.126821.

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Liu, Jiabin, Biao Li, Minglong Lei et Yong Shi. « Self-supervised knowledge distillation for complementary label learning ». Neural Networks, août 2022. http://dx.doi.org/10.1016/j.neunet.2022.08.014.

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Huang, Lang, Chao Zhang et Hongyang Zhang. « Self-Adaptive Training : Bridging Supervised and Self-Supervised Learning ». IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 1–17. http://dx.doi.org/10.1109/tpami.2022.3217792.

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Rafiei, Mohammad H., Lynne V. Gauthier, Hojjat Adeli et Daniel Takabi. « Self-Supervised Learning for Electroencephalography ». IEEE Transactions on Neural Networks and Learning Systems, 2022, 1–15. http://dx.doi.org/10.1109/tnnls.2022.3190448.

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Ye, Fei, et Adrian G. Bors. « Self-supervised adversarial variational learning ». Pattern Recognition, novembre 2023, 110156. http://dx.doi.org/10.1016/j.patcog.2023.110156.

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Verleysen, Andreas, Matthijs Biondina et Francis wyffels. « Learning self-supervised task progression metrics : a case of cloth folding ». Applied Intelligence, 2 mai 2022. http://dx.doi.org/10.1007/s10489-022-03466-8.

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