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Letteratura scientifica selezionata sul tema "SELF-ATTENTION MECHANISM"

Autore: Grafiati

Pubblicato: 11 settembre 2023

Ultima modifica: 25 luglio 2025

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Articoli di riviste sul tema "SELF-ATTENTION MECHANISM"

Gao, Shouwei, Yu Qin, Ruixin Zhu, Zirui Zhao, Hao Zhou, and Zihao Zhu. "SGSAFormer: Spike Gated Self-Attention Transformer and Temporal Attention." Electronics 14, no. 1 (2024): 43. https://doi.org/10.3390/electronics14010043.

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Spiking neural networks (SNNs), a neural network model structure inspired by the human brain, have emerged as a more energy-efficient deep learning paradigm due to their unique spike-based transmission and event-driven characteristics. Combining SNNs with the Transformer model significantly enhances SNNs’ performance while maintaining good energy efficiency. The gating mechanism, which dynamically adjusts input data and controls information flow, plays an important role in artificial neural networks (ANNs). Here, we introduce this gating mechanism into SNNs and propose a novel spike Transforme

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Yang, Kehua, Yaodong Wang, Wei Zhang, Jiqing Yao, and Yuquan Le. "Keyphrase Generation Based on Self-Attention Mechanism." Computers, Materials & Continua 61, no. 2 (2019): 569–81. http://dx.doi.org/10.32604/cmc.2019.05952.

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Yuan, Guotao, Hong Huang, and Xin Li. "Self-supervised learning backdoor defense mixed with self-attention mechanism." Journal of Computing and Electronic Information Management 12, no. 2 (2024): 81–88. http://dx.doi.org/10.54097/7hx9afkw.

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Recent studies have shown that Deep Neural Networks (DNNs) are vulnerable to backdoor attacks, where attackers embed hidden backdoors into the DNN models by poisoning a small number of training samples. The attacked models perform normally on benign samples, but when the backdoor is activated, their prediction results will be maliciously altered. To address the issues of suboptimal backdoor defense effectiveness and limited generality, a hybrid self-attention mechanism-based self-supervised learning method for backdoor defense is proposed. This method defends against backdoor attacks by levera

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Liu, Siqi, Jiangshu Wei, Gang Liu, and Bei Zhou. "Image classification model based on large kernel attention mechanism and relative position self-attention mechanism." PeerJ Computer Science 9 (April 21, 2023): e1344. http://dx.doi.org/10.7717/peerj-cs.1344.

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The Transformer has achieved great success in many computer vision tasks. With the in-depth exploration of it, researchers have found that Transformers can better obtain long-range features than convolutional neural networks (CNN). However, there will be a deterioration of local feature details when the Transformer extracts local features. Although CNN is adept at capturing the local feature details, it cannot easily obtain the global representation of features. In order to solve the above problems effectively, this paper proposes a hybrid model consisting of CNN and Transformer inspired by Vi

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Zhu, Hu, Ze Wang, Yu Shi, Yingying Hua, Guoxia Xu, and Lizhen Deng. "Multimodal Fusion Method Based on Self-Attention Mechanism." Wireless Communications and Mobile Computing 2020 (September 23, 2020): 1–8. http://dx.doi.org/10.1155/2020/8843186.

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Multimodal fusion is one of the popular research directions of multimodal research, and it is also an emerging research field of artificial intelligence. Multimodal fusion is aimed at taking advantage of the complementarity of heterogeneous data and providing reliable classification for the model. Multimodal data fusion is to transform data from multiple single-mode representations to a compact multimodal representation. In previous multimodal data fusion studies, most of the research in this field used multimodal representations of tensors. As the input is converted into a tensor, the dimensi

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Zhang, Zeyu, Bin Li, Chenyang Yan, Kengo Furuichi, and Yuki Todo. "Double Attention: An Optimization Method for the Self-Attention Mechanism Based on Human Attention." Biomimetics 10, no. 1 (2025): 34. https://doi.org/10.3390/biomimetics10010034.

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Artificial intelligence, with its remarkable adaptability, has gradually integrated into daily life. The emergence of the self-attention mechanism has propelled the Transformer architecture into diverse fields, including a role as an efficient and precise diagnostic and predictive tool in medicine. To enhance accuracy, we propose the Double-Attention (DA) method, which improves the neural network’s biomimetic performance of human attention. By incorporating matrices generated from shifted images into the self-attention mechanism, the network gains the ability to preemptively acquire informatio

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Cao, Fude, Chunguang Zheng, Limin Huang, et al. "Research of Self-Attention in Image Segmentation." Journal of Information Technology Research 15, no. 1 (2022): 1–12. http://dx.doi.org/10.4018/jitr.298619.

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Although the traditional convolutional neural network is applied to image segmentation successfully, it has some limitations. That's the context information of the long-range on the image is not well captured. With the success of the introduction of self-attentional mechanisms in the field of natural language processing (NLP), people have tried to introduce the attention mechanism in the field of computer vision. It turns out that self-attention can really solve this long-range dependency problem. This paper is a summary on the application of self-attention to image segmentation in the past tw

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Wu, Hongqiu, Ruixue Ding, Hai Zhao, Pengjun Xie, Fei Huang, and Min Zhang. "Adversarial Self-Attention for Language Understanding." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 13727–35. http://dx.doi.org/10.1609/aaai.v37i11.26608.

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Deep neural models (e.g. Transformer) naturally learn spurious features, which create a ``shortcut'' between the labels and inputs, thus impairing the generalization and robustness. This paper advances self-attention mechanism to its robust variant for Transformer-based pre-trained language models (e.g. BERT). We propose Adversarial Self-Attention mechanism (ASA), which adversarially biases the attentions to effectively suppress the model reliance on features (e.g. specific keywords) and encourage its exploration of broader semantics. We conduct comprehensive evaluation across a wide range of

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Lyu, Jiahang, Rongxin Zou, Qin Wan, et al. "Cross-and-Diagonal Networks: An Indirect Self-Attention Mechanism for Image Classification." Sensors 24, no. 7 (2024): 2055. http://dx.doi.org/10.3390/s24072055.

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In recent years, computer vision has witnessed remarkable advancements in image classification, specifically in the domains of fully convolutional neural networks (FCNs) and self-attention mechanisms. Nevertheless, both approaches exhibit certain limitations. FCNs tend to prioritize local information, potentially overlooking crucial global contexts, whereas self-attention mechanisms are computationally intensive despite their adaptability. In order to surmount these challenges, this paper proposes cross-and-diagonal networks (CDNet), innovative network architecture that adeptly captures global

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Wang, Baoyun, Lei He, Linwei Song, Rui Niu, and Ming Cheng. "Attention-Linear Trajectory Prediction." Sensors 24, no. 20 (2024): 6636. http://dx.doi.org/10.3390/s24206636.

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Recently, a large number of Transformer-based solutions have emerged for the trajectory prediction task, but there are shortcomings in the effectiveness of Transformers in trajectory prediction. Specifically, while position encoding preserves some of the ordering information, the self-attention mechanism at the core of the Transformer has its alignment invariance that leads to the loss of temporal information, which is crucial for trajectory prediction. For this reason, we design a simple and efficient strategy for temporal information extraction and prediction of trajectory sequences using th

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Tesi sul tema "SELF-ATTENTION MECHANISM"

Raykos, Bronwyn C. "Attentional and interpretive biases : independent dimensions of individual difference or expressions of a common selective processing mechanism?" University of Western Australia. School of Psychology, 2007. http://theses.library.uwa.edu.au/adt-WU2007.0018.

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[Truncated abstract] Attentional and interpretive biases are important dimensions of individual difference that have been implicated in the etiology and maintenance of a range of clinical problems. Yet there has been no systematic investigation into the relationship between these dimensions of individual difference. The current research program tested predictions derived from two competing theoretical accounts of the relationship between attentional and interpretive biases. The Common Mechanism Account proposes that cognitive biases represent concurrent manifestations of a single underlying se

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Raykos, Bronwyn C. "Attentional and interpretive biases : independent dimensions of individual difference or expressions of a common selective processing mechanism? /." Connect to this title, 2006. http://theses.library.uwa.edu.au/adt-WU2007.0018.

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SACHDEVA, NITIN. "CYBERBULLYING DETECTION ON SOCIAL MEDIA USING DEEP LEARNING MODELS." Thesis, DELHI TECHNOLOGICAL UNIVERSITY, 2021. http://dspace.dtu.ac.in:8080/jspui/handle/repository/18914.

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Application of deep learning models for cyberbullying detection in social media is an upcoming area for both researchers and practitioners for finding, exploring and analysing the extensibility of human-based expressions. Automated cyberbullying detection is typically a classification problem in natural language processing where the intent is to classify each abusive or offensive comment or post or message or image as either bullying or non-bullying. It needs high-level semantic analysis as well. Most of the earlier attempts on cyberbullying detection rely on manual feature extraction me

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Govindarajan, Hariprasath. "Self-Supervised Representation Learning for Content Based Image Retrieval." Thesis, Linköpings universitet, Statistik och maskininlärning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166223.

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Automotive technologies and fully autonomous driving have seen a tremendous growth in recent times and have benefitted from extensive deep learning research. State-of-the-art deep learning methods are largely supervised and require labelled data for training. However, the annotation process for image data is time-consuming and costly in terms of human efforts. It is of interest to find informative samples for labelling by Content Based Image Retrieval (CBIR). Generally, a CBIR method takes a query image as input and returns a set of images that are semantically similar to the query image. The

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Moadab, Ida. "The Role of Mindfulness and Self-Compassion in the Neural Mechanisms of Attention and Self-Monitoring." Thesis, University of Oregon, 2013. http://hdl.handle.net/1794/13402.

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The present study sought to investigate the effects of meditation practice on the neural mechanisms of attention and self-monitoring by comparing a group of experienced meditators to matched controls. Self-report measures of mindfulness and self-compassion were assessed to examine whether meditation-related improvements in attention and self-monitoring were linked to increases in these qualities. Thus, differences between groups (meditator versus control) on all variables and relationships among variables (attention, self-monitoring, self-compassion, and mindfulness) were explored. Results i

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Bakkali, Souhail. "Multimodal Document Understanding with Unified Vision and Language Cross-Modal Learning." Electronic Thesis or Diss., La Rochelle, 2022. http://www.theses.fr/2022LAROS046.

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Les modèles développés dans cette thèse sont le résultat d'un processus itératif d'analyse et de synthèse entre les théories existantes et nos études réalisées. Plus spécifiquement, nous souhaitons étudier l'apprentissage inter-modal pour la compréhension contextualisée sur les composants des documents à travers le langage et la vision. Cette thèse porte sur l'avancement de la recherche sur l'apprentissage inter-modal et apporte des contributions sur quatre fronts : (i) proposer une approche inter-modale avec des réseaux profonds pour exploiter conjointement les informations visuelles et textu

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Pelloin, Valentin. "La compréhension de la parole dans les systèmes de dialogues humain-machine à l'heure des modèles pré-entraînés." Electronic Thesis or Diss., Le Mans, 2024. http://www.theses.fr/2024LEMA1002.

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Dans cette thèse, la compréhension automatique de la parole (SLU) est étudiée dans le cadre applicatif de dialogues téléphoniques à buts définis (réservation de chambres d'hôtel par exemple). Historiquement, la SLU était réalisée en cascade : un système de reconnaissance de la parole réalisait une transcription en mots, puis un système de compréhension y associait une annotation sémantique. Le développement des méthodes neuronales profondes a fait émerger les architectures de bout-en-bout, où la tâche de compréhension est réalisée par un système unique, appliqué directement à partir du signal

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Yeh, Han-Yun, and 葉瀚允. "End-to-end Pinyin to Character Language Model using Self-Attention Mechanism." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/vk3zt8.

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碩士 國立交通大學 電信工程研究所 107 Deep nerual network with conventional automatic speech recognition structure has achieved huge improvement. Similarly, end-to-end speech recongnition structure got close performance in these two years, but with huge amout of data and computing resources. This study attempt to focus on end-to-end language model, training an end-to-end language model by sequence labeling method and self-attention seq2seq model (Transformer) which are common method in some NLP task, with syllable sequence converted from 440 million words chinese corpus through a proposed G2P syst

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Sarzedas, João Pereira. "The impact of hallucination predisposition on vocal perception mechanisms : an ERP study of attention, identity and emotion." Master's thesis, 2020. http://hdl.handle.net/10451/45746.

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Tese de mestrado, Psicopatologia, Universidade de Lisboa, Faculdade de Medicina, 2020 In recent years, a more dimensional view of psychotic disorders has been supported by several epidemiological studies showing the existence of a continuum of psychotic like-experiences in the general population. Reported similarities between the cognitive and neural mechanisms underpinning hallucinatory experiences in psychotic and in individuals with high hallucination predisposition provide further support for this psychosis continuum model. In the auditory domain, schizophrenic patients show altered per

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Libri sul tema "SELF-ATTENTION MECHANISM"

Mironova, Svetlana. The doctrine of the financial and legal status of municipalities in the Russian Federation and its implementation. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1819040.

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The monograph forms the doctrine of the financial and legal status of municipalities and identifies the features of its implementation in the Russian Federation at the present stage. The structure of the financial and legal status of the municipality as a whole is comprehensively presented, its elements are defined, their detailed characteristics are given; the features of the implementation of the financial and legal status of certain types of municipalities (urban and municipal districts, municipal districts, urban and rural settlements, etc.), those of them that have a special legal status

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Avhadeev, V., L. Bitkova, C. Bogolyubov, et al. Implementation of the Law on Responsible Treatment of Animals: from the quality of norms to effective law enforcement. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1410760.

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The collection contains articles on the quality of the conceptual apparatus and terminology of Federal Law No. 498-FZ of December 27, 2018 "On Responsible Treatment of Animals and on Amendments to Certain Legislative Acts of the Russian Federation", the subject of its legal regulation, the effectiveness of the mechanism for its implementation laid down in the law, state supervision and public control in the field of animal treatment. The problems of organizing the activities of animal shelters without owners, protecting animals from abuse and responsibility for such offenses, directions and wa

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Andrichenko, Lyudmila, A. Postnikov, L. Vasil'eva, Zh Gaunova, E. Nikitina, and Inna Plyugina. Reform of the organization of public power: the main directions of implementation. INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1839416.

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The monograph examines topical issues of reforming the organization of public power in our country in connection with the adoption in 2020 of the Law on Amendments to the Constitution of the Russian Federation. The logic of changes in the organization of public power and the directions of concretization of constitutional values, taking into account the laws of the development of the constitutional system of Russia, are revealed. The most significant characteristics of the updated model of interaction of federal public authorities in accordance with the principle of separation of powers a

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Karzaeva, Natalya. Fundamentals of economic security. 2nd ed. INFRA-M Academic Publishing LLC., 2024. http://dx.doi.org/10.12737/2131554.

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The textbook reveals the main categories and concepts in the field of security, the goals and objectives of the organization of economic security, the functions of subjects of economic security. Special attention is paid to threats and risks, objects, subjects, mechanisms for ensuring industrial, financial, information and personnel security of business entities. Each chapter contains tests and questions for self-checking the assimilation of educational material, as well as a list of recommended additional literature. Meets the requirements of the federal state educational standards of higher

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Sazhina, Muza, Anna Kashirova, Stanislav Makarov, and Egor Osiop. The social wealth of the innovation system. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1875920.

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The monograph reveals the key socio-economic problems of the innovation economy: its content as a knowledge economy and its role in evolutionary development; human capital (living intelligence) as the main resource of the innovation economy. Much attention is paid to the institutional support of innovation through a system of institutions and mutually beneficial contracts. The mixed mechanism of implementation of innovative activity as a synthesis of spontaneous market self-regulation and conscious public administration is shown. The result of the "social control" of society and the state is t

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Sorokina, Viktoriya, Elena Ivanova, and Sergey Enikolopov. Psychological factors of rehabilitation potential in patients with depressive syndrome. INFRA-M Academic Publishing LLC., 2025. https://doi.org/10.12737/2188421.

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This monograph is devoted to the study of the rehabilitation potential of the personality of patients with depressive syndrome, which occurs both in mental illness and after brain injuries. The main attention is paid to the study of personal characteristics that can act as key factors for the development of effective rehabilitation programs. The concept of the "rehabilitation potential of a personality" is highlighted and substantiated as a summary characteristic that determines the patient's ability to psychological and functional recovery. The psychological features influencing the rehabilit

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Crowell, Sheila E., Mona Yaptangco, and Sara L. Turner. Coercion, Invalidation, and Risk for Self-Injury and Borderline Personality Traits. Edited by Thomas J. Dishion and James Snyder. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199324552.013.16.

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Self-inflicted injury (SII) is defined as a deliberate act in which a person seeks to cause bodily harm or death. The etiology and developmental course of SII are unclear. Converging evidence suggests coercive family processes may heighten risk for SII and related clinical problems among vulnerable youth. This chapter outlines a developmental theory of SII with particular attention to contextual risk factors. It proposes that risk for SII is highest when vulnerable youth are exposed repeatedly to coercive and invalidating family environments. Evidence in support of this theory is drawn from lo

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O'Callaghan, Claire, and Muireann Irish. Candidate Mechanisms of Spontaneous Cognition as Revealed by Dementia Syndromes. Edited by Kalina Christoff and Kieran C. R. Fox. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190464745.013.6.

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The capacity to engage in spontaneous self-generated thought is fundamental to the human experience, yet surprisingly little is known regarding the neurocognitive mechanisms that support this complex ability. Dementia syndromes offer a unique opportunity to study how the breakdown of large-scale functional brain networks impacts spontaneous cognition. Indeed, many of the characteristic cognitive changes in dementia reflect the breakdown of foundational processes essential for discrete aspects of self-generated thought. This chapter discusses how disease-specific alterations in memory-based/con

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Hasenkamp, Wendy. Catching the Wandering Mind. Edited by Kalina Christoff and Kieran C. R. Fox. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780190464745.013.12.

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This chapter considers a form of attention-based meditation as a novel means to gain insight into the mechanisms and phenomenology of spontaneous thought. Focused attention (FA) meditation involves keeping one’s attention on a chosen object, and repeatedly catching the mind when it strays from the object into spontaneous thought. This practice can thus be viewed as a kind of self-caught mind-wandering paradigm, which suggests it may have great utility for research on spontaneous thought. Current findings about the effects of meditation on mind-wandering and meta-awareness are reviewed, and imp

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Blackmore, Susan. Consciousness: A Very Short Introduction. Oxford University Press, 2017. http://dx.doi.org/10.1093/actrade/9780198794738.001.0001.

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Consciousness is ‘the last great mystery for science’. How can a physical brain create our experience of the world? What creates our identity? Do we really have free will? Could consciousness itself be an illusion? Exciting new developments in brain science are continuing these debates, and the field has now expanded to include biologists, neuroscientists, psychologists, and philosophers. Consciousness: A Very Short Introduction clarifies the potentially confusing arguments, and the major theories, whilst also outlining the amazing pace of neuroscience discoveries. Covering areas such as const

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Capitoli di libri sul tema "SELF-ATTENTION MECHANISM"

Hossain, Syed Md Minhaz, Anik Sen, and Kaushik Deb. "Detecting Spam SMS Using Self Attention Mechanism." In Intelligent Computing & Optimization. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19958-5_17.

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Daanouni, Othmane, Bouchaib Cherradi, and Amal Tmiri. "Self-Attention Mechanism for Diabetic Retinopathy Detection." In Emerging Trends in ICT for Sustainable Development. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-53440-0_10.

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Feng, Zekai, Hong Jia, and Mengke Li. "Long-Tailed Recognition Based on Self-attention Mechanism." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5666-7_32.

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Zhang, Jiaxin, Yinshan Jia, and Hongfei Yu. "Application Research of YOLOv3 Incorporating Self-attention Mechanism." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3927-3_45.

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Kanno, Satoki, and Mamoru Mimura. "Detection of Malware Using Self-Attention Mechanism and Strings." In Network and System Security. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39828-5_3.

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Luo, Youtao, and Xiaoming Gao. "Lightweight Human Pose Estimation Based on Self-Attention Mechanism." In Multidimensional Signals, Augmented Reality and Information Technologies. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-7011-7_19.

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Yuan, Genji, Jianbo Li, Yue Wang, and Xianglong Meng. "Incorporating Self Attention Mechanism into Semantic Segmentation for Lane Detection." In Wireless Algorithms, Systems, and Applications. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-19214-2_37.

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An, MingShou, Hye-Youn Lim, YunChuan He, and Dae-Seong Kang. "Fusion Self-attention Feature Clustering Mechanism Network for Person ReID." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9416-8_55.

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Li, Xiang, Ning Sun, Jixin Liu, Lei Chai, and Haian Sun. "Multi-modal Scene Recognition Based on Global Self-attention Mechanism." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20738-9_14.

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Cvitanović, Ivana, and Marina Bagić Babac. "Deep Learning with Self-Attention Mechanism for Fake News Detection." In Studies in Computational Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-90087-8_10.

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Atti di convegni sul tema "SELF-ATTENTION MECHANISM"

Mario, Viti, Nadiya Shvai, Arcadi Llanza, and Amir Nakib. "A 0-Shot Self-Attention Mechanism for Accelerated Diagonal Attention." In 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). IEEE, 2025. https://doi.org/10.1109/wacv61041.2025.00710.

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Jia, Ning, Mengshi Li, and Nan Yin. "IST-OAM:Image Style Transfer via Optimized Self-Attention Mechanism." In 2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2024. http://dx.doi.org/10.1109/itnec60942.2024.10733239.

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Damara, Fadli, Zoran Utkovski, and Slawomir Stanczak. "Signal Separation in Radio Spectrum Using Self-Attention Mechanism." In 2024 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW). IEEE, 2024. http://dx.doi.org/10.1109/icasspw62465.2024.10627553.

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Kulkarni, Sarthak, Dinesh Kumar Vishwakarma, and Virender Ranga. "Multi-Head Self-Attention Mechanism for Enhanced Deepfake Detection." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N). IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10895854.

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Du, Xuran, and Gang Gan. "Network Intrusion Detection Based on Self-Attention Mechanism and BIGRU." In 2024 2nd International Conference On Mobile Internet, Cloud Computing and Information Security (MICCIS). IEEE, 2024. http://dx.doi.org/10.1109/miccis63508.2024.00045.

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Zhou, Ming, Hancheng Yu, Zhongchen Li, and Yupu Zhang. "Self-supervised monocular depth estimation based on cross-attention mechanism." In 2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE). IEEE, 2024. https://doi.org/10.1109/icemce64157.2024.10861911.

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Li, Borui, Yongli Wang, Dongmei Liu, and Anqi Huang. "Traffic Signal Control Approach Combining Qmix and Self-Attention Mechanism." In 2024 6th International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI). IEEE, 2024. https://doi.org/10.1109/mlbdbi63974.2024.10823864.

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Vo, Tan Nhat, Bao Duc Ngo, and Len Tien Bui. "Super Resolution Using Convolutional Neural Network and Self-Attention Mechanism." In 2024 RIVF International Conference on Computing and Communication Technologies (RIVF). IEEE, 2024. https://doi.org/10.1109/rivf64335.2024.11009104.

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S, Vasuki, Janani V G, Rigveda B R D, Sabreen Safira S, and Srimathi R. "An Underwater Species Detection with YOLOv9 Combining Self Attention Mechanism." In 2025 International Conference on Electronics, Computing, Communication and Control Technology (ICECCC). IEEE, 2025. https://doi.org/10.1109/iceccc65144.2025.11064246.

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Zhang, Shanqiang, Wei Chen, Chunyao Yang, and Hao Liu. "CNN-LSTM Unmanned Vehicle Path Planning Based on Self-Attention Mechanism." In 2024 IEEE International Conference on Unmanned Systems (ICUS). IEEE, 2024. https://doi.org/10.1109/icus61736.2024.10839982.

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Rapporti di organizzazioni sul tema "SELF-ATTENTION MECHANISM"

Chornodon, Myroslava. FEAUTURES OF GENDER IN MODERN MASS MEDIA. Ivan Franko National University of Lviv, 2021. http://dx.doi.org/10.30970/vjo.2021.49.11064.

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Abstract (sommario):

The article clarifies of gender identity stereotypes in modern media. The main gender stereotypes covered in modern mass media are analyzed and refuted. The model of gender relations in the media is reflected mainly in the stereotypical images of men and woman. The features of the use of gender concepts in modern periodicals for women and men were determined. The most frequently used derivatives of these macroconcepts were identified and analyzed in detail. It has been found that publications for women and men are full of various gender concepts that are used in different contexts. Ingeneral,

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