Results for 'Deep Learning'

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  1. Deep Learning: A Philosophical Introduction.Cameron Buckner - 2019 - Philosophy Compass 14 (10).
  2. Using Deep Learning to Detect Facial Markers of Complex Decision Making.Gianluca Guglielmo, Irene Font Peradejordi & Michal Klincewicz - 2022 - In C. Browne, A. Kishimoto & J. Schaeffer (eds.), Advances in Computer Games. ACG 2021. Lecture Notes in Computer Science. Springer. pp. 187-196.
    In this paper, we report on an experiment with The Walking Dead (TWD), which is a narrative-driven adventure game where players have to survive in a post-apocalyptic world filled with zombies. We used OpenFace software to extract action unit (AU) intensities of facial expressions characteristic of decision-making processes and then we implemented a simple convolution neural network (CNN) to see which AUs are predictive of decision-making. Our results provide evidence that the pre-decision variations in action units 17 (chin raiser), 23 (...)
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    Deep Learning and Linguistic Representation.Shalom Lappin - 2021 - Chapman & Hall/Crc.
    The application of deep learning methods to problems in natural language processing has generated significant progress across a wide range of natural language processing tasks. For some of these applications, deep learning models now approach or surpass human performance. While the success of this approach has transformed the engineering methods of machine learning in artificial intelligence, the significance of these achievements for the modelling of human learning and representation remains unclear. Deep Learning (...)
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  4.  90
    Deep Learning in Law: Early Adaptation and Legal Word Embeddings Trained on Large Corpora.Ilias Chalkidis & Dimitrios Kampas - 2019 - Artificial Intelligence and Law 27 (2):171-198.
    Deep Learning has been widely used for tackling challenging natural language processing tasks over the recent years. Similarly, the application of Deep Neural Networks in legal analytics has increased significantly. In this survey, we study the early adaptation of Deep Learning in legal analytics focusing on three main fields; text classification, information extraction, and information retrieval. We focus on the semantic feature representations, a key instrument for the successful application of deep learning in (...)
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    Deep Learning in Law: Early Adaptation and Legal Word Embeddings Trained on Large Corpora.Ilias Chalkidis & Dimitrios Kampas - 2019 - Artificial Intelligence and Law 27 (2):171-198.
    Deep Learning has been widely used for tackling challenging natural language processing tasks over the recent years. Similarly, the application of Deep Neural Networks in legal analytics has increased significantly. In this survey, we study the early adaptation of Deep Learning in legal analytics focusing on three main fields; text classification, information extraction, and information retrieval. We focus on the semantic feature representations, a key instrument for the successful application of deep learning in (...)
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  6.  11
    Deep Learning Algorithms and Multicriteria Decision-Making Used in Big Data: A Systematic Literature Review.Mei Yang, Shah Nazir, Qingshan Xu & Shaukat Ali - 2020 - Complexity 2020:1-18.
    The data are ever increasing with the increase in population, communication of different devices in networks, Internet of Things, sensors, actuators, and so on. This increase goes into different shapes such as volume, velocity, variety, veracity, and value extracting meaningful information and insights, all are challenging tasks and burning issues. Decision-making based on multicriteria is one of the most critical issues solving ways to select the most suitable decision among a number of alternatives. Deep learning algorithms and multicriteria-based (...)
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  7.  13
    Understanding Deep Learning with Statistical Relevance.Tim Räz - 2022 - Philosophy of Science 89 (1):20-41.
    This paper argues that a notion of statistical explanation, based on Salmon’s statistical relevance model, can help us better understand deep neural networks. It is proved that homogeneous partitions, the core notion of Salmon’s model, are equivalent to minimal sufficient statistics, an important notion from statistical inference. This establishes a link to deep neural networks via the so-called Information Bottleneck method, an information-theoretic framework, according to which deep neural networks implicitly solve an optimization problem that generalizes minimal (...)
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  8. AI-Completeness: Using Deep Learning to Eliminate the Human Factor.Kristina Šekrst - 2020 - In Sandro Skansi (ed.), Guide to Deep Learning Basics. Springer. pp. 117-130.
    Computational complexity is a discipline of computer science and mathematics which classifies computational problems depending on their inherent difficulty, i.e. categorizes algorithms according to their performance, and relates these classes to each other. P problems are a class of computational problems that can be solved in polynomial time using a deterministic Turing machine while solutions to NP problems can be verified in polynomial time, but we still do not know whether they can be solved in polynomial time as well. A (...)
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    Deep Learning and Cognitive Science.Pietro Perconti & Alessio Plebe - 2020 - Cognition 203:104365.
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  10.  12
    Deep Learning and Synthetic Media.Raphaël Millière - 2022 - Synthese 200 (3):1-27.
    Deep learning algorithms are rapidly changing the way in which audiovisual media can be produced. Synthetic audiovisual media generated with deep learning—often subsumed colloquially under the label “deepfakes”—have a number of impressive characteristics; they are increasingly trivial to produce, and can be indistinguishable from real sounds and images recorded with a sensor. Much attention has been dedicated to ethical concerns raised by this technological development. Here, I focus instead on a set of issues related to the (...)
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  11. Potato Classification Using Deep Learning.Abeer A. Elsharif, Ibtesam M. Dheir, Alaa Soliman Abu Mettleq & Samy S. Abu-Naser - 2020 - International Journal of Academic Pedagogical Research (IJAPR) 3 (12):1-8.
    Abstract: Potatoes are edible tubers, available worldwide and all year long. They are relatively cheap to grow, rich in nutrients, and they can make a delicious treat. The humble potato has fallen in popularity in recent years, due to the interest in low-carb foods. However, the fiber, vitamins, minerals, and phytochemicals it provides can help ward off disease and benefit human health. They are an important staple food in many countries around the world. There are an estimated 200 varieties of (...)
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  12. Handwritten Signature Verification Using Deep Learning.Eman Alajrami, Belal A. M. Ashqar, Bassem S. Abu-Nasser, Ahmed J. Khalil, Musleh M. Musleh, Alaa M. Barhoom & Samy S. Abu-Naser - 2020 - International Journal of Academic Multidisciplinary Research (IJAMR) 3 (12):39-44.
    Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a (...)
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  13.  7
    Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization.Xue-Bo Jin, Hong-Xing Wang, Xiao-Yi Wang, Yu-Ting Bai, Ting-Li Su & Jian-Lei Kong - 2020 - Complexity 2020:1-14.
    The power load prediction is significant in a sustainable power system, which is the key to the energy system’s economic operation. An accurate prediction of the power load can provide a reliable decision for power system planning. However, it is challenging to predict the power load with a single model, especially for multistep prediction, because the time series load data have multiple periods. This paper presents a deep hybrid model with a serial two‐level decomposition structure. First, the power load (...)
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  14. Lemon Classification Using Deep Learning.Jawad Yousif AlZamily & Samy Salim Abu Naser - 2020 - International Journal of Academic Pedagogical Research (IJAPR) 3 (12):16-20.
    Abstract : Background: Vegetable agriculture is very important to human continued existence and remains a key driver of many economies worldwide, especially in underdeveloped and developing economies. Objectives: There is an increasing demand for food and cash crops, due to the increasing in world population and the challenges enforced by climate modifications, there is an urgent need to increase plant production while reducing costs. Methods: In this paper, Lemon classification approach is presented with a dataset that contains approximately 2,000 images (...)
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  15.  8
    A Deep Learning Approach for a Source Code Detection Model Using Self-Attention.Yao Meng & Long Liu - 2020 - Complexity 2020:1-15.
    With the development of deep learning, many approaches based on neural networks are proposed for code clone. In this paper, we propose a novel source code detection model At-biLSTM based on a bidirectional LSTM network with a self-attention layer. At-biLSTM is composed of a representation model and a discriminative model. The representation model firstly transforms the source code into an abstract syntactic tree and splits it into a sequence of statement trees; then, it encodes each of the statement (...)
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  16.  12
    Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification.Zeynep H. Kilimci & Selim Akyokus - 2018 - Complexity 2018:1-10.
    The use of ensemble learning, deep learning, and effective document representation methods is currently some of the most common trends to improve the overall accuracy of a text classification/categorization system. Ensemble learning is an approach to raise the overall accuracy of a classification system by utilizing multiple classifiers. Deep learning-based methods provide better results in many applications when compared with the other conventional machine learning algorithms. Word embeddings enable representation of words learned from (...)
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  17.  19
    Complex Deep Learning and Evolutionary Computing Models in Computer Vision.Li Zhang, Chee Peng Lim & Jungong Han - 2019 - Complexity 2019:1-2.
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  18.  28
    Deep Learning, Education and the Final Stage of Automation.Michael A. Peters - 2018 - Educational Philosophy and Theory 50 (6-7):549-553.
  19.  4
    Hybridized Deep Learning Model for Perfobond Rib Shear Strength Connector Prediction.Jamal Abdulrazzaq Khalaf, Abeer A. Majeed, Mohammed Suleman Aldlemy, Zainab Hasan Ali, Ahmed W. Al Zand, S. Adarsh, Aissa Bouaissi, Mohammed Majeed Hameed & Zaher Mundher Yaseen - 2021 - Complexity 2021:1-21.
    Accurate and reliable prediction of Perfobond Rib Shear Strength Connector is considered as a major issue in the structural engineering sector. Besides, selecting the most significant variables that have a major influence on PRSC in every important step for attaining economic and more accurate predictive models, this study investigates the capacity of deep learning neural network for shear strength prediction of PRSC. The proposed DLNN model is validated against support vector regression, artificial neural network, and M5 tree model. (...)
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  20.  17
    Using Deep Learning to Predict Complex Systems: A Case Study in Wind Farm Generation.J. M. Torres & R. M. Aguilar - 2018 - Complexity 2018:1-10.
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  21.  18
    (What) Can Deep Learning Contribute to Theoretical Linguistics?Gabe Dupre - 2021 - Minds and Machines 31 (4):617-635.
    Deep learning techniques have revolutionised artificial systems’ performance on myriad tasks, from playing Go to medical diagnosis. Recent developments have extended such successes to natural language processing, an area once deemed beyond such systems’ reach. Despite their different goals, these successes have suggested that such systems may be pertinent to theoretical linguistics. The competence/performance distinction presents a fundamental barrier to such inferences. While DL systems are trained on linguistic performance, linguistic theories are aimed at competence. Such a barrier (...)
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  22.  14
    Using Deep Learning to Predict Sentiments: Case Study in Tourism.C. A. Martín, J. M. Torres, R. M. Aguilar & S. Diaz - 2018 - Complexity 2018:1-9.
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  23.  55
    Computational Functionalism for the Deep Learning Era.Ezequiel López-Rubio - 2018 - Minds and Machines 28 (4):667-688.
    Deep learning is a kind of machine learning which happens in a certain type of artificial neural networks called deep networks. Artificial deep networks, which exhibit many similarities with biological ones, have consistently shown human-like performance in many intelligent tasks. This poses the question whether this performance is caused by such similarities. After reviewing the structure and learning processes of artificial and biological neural networks, we outline two important reasons for the success of (...) learning, namely the extraction of successively higher level features and the multiple layer structure, which are closely related to each other. Then some indications about the framing of this heated debate are given. After that, an assessment of the value of artificial deep networks as models of the human brain is given from the similarity perspective of model representation. Finally, a new version of computational functionalism is proposed which addresses the specificity of deep neural computation better than classic, program based computational functionalism. (shrink)
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  24.  10
    DeepRhole: Deep Learning for Rhetorical Role Labeling of Sentences in Legal Case Documents.Paheli Bhattacharya, Shounak Paul, Kripabandhu Ghosh, Saptarshi Ghosh & Adam Wyner - forthcoming - Artificial Intelligence and Law:1-38.
    The task of rhetorical role labeling is to assign labels to sentences of a court case document. Rhetorical role labeling is an important problem in the field of Legal Analytics, since it can aid in various downstream tasks as well as enhances the readability of lengthy case documents. The task is challenging as case documents are highly various in structure and the rhetorical labels are often subjective. Previous works for automatic rhetorical role identification mainly used Conditional Random Fields over manually (...)
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  25.  11
    Deep Learning Applied to Seismic Attribute Computation.Donald P. Griffith, S. Ahmad Zamanian, Jeremy Vila, Antoine Vial-Aussavy, John Solum, R. David Potter & Francesco Menapace - 2019 - Interpretation 7 (3):SE141-SE150.
    We have trained deep convolutional neural networks to accelerate the computation of seismic attributes by an order of magnitude. These results are enabled by overcoming the prohibitive memory requirements typical of 3D DCNs for segmentation and regression by implementing a novel, memory-efficient 3D-to-2D convolutional architecture and by including tens of thousands of synthetically generated labeled examples to enhance DCN training. Including diverse synthetic labeled seismic in training helps the network generalize enabling it to accurately predict seismic attribute values on (...)
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  26.  4
    Deep Learning of Shared Perceptual Representations for Familiar and Unfamiliar Faces: Reply to Commentaries.Nicholas M. Blauch, Marlene Behrmann & David C. Plaut - 2021 - Cognition 208:104484.
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  27.  3
    Deep Learning-Based Efficient Model Development for Phishing Detection Using Random Forest and BLSTM Classifiers.Shan Wang, Sulaiman Khan, Chuyi Xu, Shah Nazir & Abdul Hafeez - 2020 - Complexity 2020:1-7.
    With the increase in the number of electronic devices and developments in the communication system, security becomes one of the challenging issues. Users are interacting with each other through different heterogeneous devices such as smart sensors, actuators, and many other devices to process, monitor, and communicate different scenarios of real life. Such communication needs a secure medium through which users can communicate in a secure and reliable way so that their information may not be lost. The proposed study is an (...)
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    Deep Learning Based Proactive Caching for Effective WSN-Enabled Vision Applications.Fangyuan Lei, Jun Cai, Qingyun Dai & Huimin Zhao - 2019 - Complexity 2019:1-12.
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  29. Applying Deep Learning in the Training of Communication Design Talents Under University-Industrial Research Collaboration.Rui Zhou, Zhihua He, Xiaobiao Lu & Ying Gao - 2021 - Frontiers in Psychology 12.
    The purpose of the study was to solve the problem of the mismatching between the supply and demand of the talents that universities provide for society, whose major is communication design. The correlations between social post demand and university cultivation, as well as between social post demand and the demand indexes of enterprises for posts, are explored under the guidance of University-Industrial Research Collaboration. The backpropagation neural network is used, and the advantages of the Seasonal Autoregressive Integrated Moving Average model (...)
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    Deep Learning-Based Intelligent Robot in Sentencing.Xuan Chen - 2022 - Frontiers in Psychology 13.
    This work aims to explore the application of deep learning-based artificial intelligence technology in sentencing, to promote the reform and innovation of the judicial system. First, the concept and the principles of sentencing are introduced, and the deep learning model of intelligent robot in trials is proposed. According to related concepts, the issues that need to be solved in artificial intelligence sentencing based on deep learning are introduced. The deep learning model is (...)
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  31.  8
    Deep Learning Approach to Text Analysis for Human Emotion Detection From Big Data.Jia Guo - 2022 - Journal of Intelligent Systems 31 (1):113-126.
    Emotional recognition has arisen as an essential field of study that can expose a variety of valuable inputs. Emotion can be articulated in several means that can be seen, like speech and facial expressions, written text, and gestures. Emotion recognition in a text document is fundamentally a content-based classification issue, including notions from natural language processing and deep learning fields. Hence, in this study, deep learning assisted semantic text analysis has been proposed for human emotion detection (...)
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  32.  9
    Is Deep Learning for Image Recognition Applicable to Stock Market Prediction?Hyun Sik Sim, Hae In Kim & Jae Joon Ahn - 2019 - Complexity 2019:1-10.
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  33. Type of Tomato Classification Using Deep Learning.Mahmoud A. Alajrami & Samy S. Abu-Naser - 2020 - International Journal of Academic Pedagogical Research (IJAPR) 3 (12):21-25.
    Abstract: Tomatoes are part of the major crops in food security. Tomatoes are plants grown in temperate and hot regions of South American origin from Peru, and then spread to most countries of the world. Tomatoes contain a lot of vitamin C and mineral salts, and are recommended for people with constipation, diabetes and patients with heart and body diseases. Studies and scientific studies have proven the importance of eating tomato juice in reducing the activity of platelets in diabetics, which (...)
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  34.  8
    Developing an Efficient Deep Learning-Based Trusted Model for Pervasive Computing Using an LSTM-Based Classification Model.Yang He, Shah Nazir, Baisheng Nie, Sulaiman Khan & Jianhui Zhang - 2020 - Complexity 2020:1-6.
    Mobile and pervasive computing is one of the recent paradigms available in the area of information technology. The role of pervasive computing is foremost in the field where it provides the ability to distribute computational services to the surroundings where people work and leads to issues such as trust, privacy, and identity. To provide an optimal solution to these generic problems, the proposed research work aims to implement a deep learning-based pervasive computing architecture to address these problems. Long (...)
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  35. Applying Deep Learning Techniques to Estimate Patterns of Musical Gesture.David Dalmazzo, George Waddell & Rafael Ramírez - 2021 - Frontiers in Psychology 11.
    Repetitive practice is one of the most important factors in improving the performance of motor skills. This paper focuses on the analysis and classification of forearm gestures in the context of violin playing. We recorded five experts and three students performing eight traditional classical violin bow-strokes: martelé, staccato, detaché, ricochet, legato, trémolo, collé, and col legno. To record inertial motion information, we utilized the Myo sensor, which reports a multidimensional time-series signal. We synchronized inertial motion recordings with audio data to (...)
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  36.  15
    Deep Learning Based Part-of-Speech Tagging for Malayalam Twitter Data.S. Kumar, M. Anand Kumar & K. P. Soman - 2019 - Journal of Intelligent Systems 28 (3):423-435.
    The paper addresses the problem of part-of-speech tagging for Malayalam tweets. The conversational style of posts/tweets/text in social media data poses a challenge in using general POS tagset for tagging the text. For the current work, a tagset was designed that contains 17 coarse tags and 9915 tweets were tagged manually for experiment and evaluation. The tagged data were evaluated using sequential deep learning methods like recurrent neural network, gated recurrent units, long short-term memory, and bidirectional LSTM. The (...)
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  37.  20
    Deep-Learning Networks and the Functional Architecture of Executive Control.Richard P. Cooper - 2017 - Behavioral and Brain Sciences 40.
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  38. Deep Learning Based Emotion Recognition and Visualization of Figural Representation.Xiaofeng Lu - 2022 - Frontiers in Psychology 12.
    This exploration aims to study the emotion recognition of speech and graphic visualization of expressions of learners under the intelligent learning environment of the Internet. After comparing the performance of several neural network algorithms related to deep learning, an improved convolution neural network-Bi-directional Long Short-Term Memory algorithm is proposed, and a simulation experiment is conducted to verify the performance of this algorithm. The experimental results indicate that the Accuracy of CNN-BiLSTM algorithm reported here reaches 98.75%, which is (...)
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  39. Deep Learning Course Development and Evaluation of Artificial Intelligence in Vocational Senior High Schools.Chih-Cheng Tsai, Chih-Chao Chung, Yuh-Ming Cheng & Shi-Jer Lou - 2022 - Frontiers in Psychology 13.
    This study aimed to develop cross-domain deep learning courses of artificial intelligence in vocational senior high schools and explore its impact on students’ learning effects. It initially adopted a literature review to develop a cross-domain SPOC-AIoT Course with SPOC and the Double Diamond 4D model in vocational senior high schools. Afterward, it adopted participatory action research and a questionnaire survey and conducted analyses on the various aspects of the technology acceptance model by SmartPLS. Further, this study explored (...)
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  40.  5
    Deep Learning Meets Deep Democracy: Deliberative Governance and Responsible Innovation in Artificial Intelligence.Alexander Buhmann & Christian Fieseler - forthcoming - Business Ethics Quarterly:1-34.
    Responsible innovation in artificial intelligence calls for public deliberation: well-informed “deep democratic” debate that involves actors from the public, private, and civil society sectors in joint efforts to critically address the goals and means of AI. Adopting such an approach constitutes a challenge, however, due to the opacity of AI and strong knowledge boundaries between experts and citizens. This undermines trust in AI and undercuts key conditions for deliberation. We approach this challenge as a problem of situating the knowledge (...)
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  41. A Deep Learning-Based Sentiment Classification Model for Real Online Consumption.Yang Su & Yan Shen - 2022 - Frontiers in Psychology 13.
    Most e-commerce platforms allow consumers to post product reviews, causing more and more consumers to get into the habit of reading reviews before they buy. These online reviews serve as an emotional feedback of consumers’ product experience and contain a lot of important information, but inevitably there are malicious or irrelevant reviews. It is especially important to discover and identify the real sentiment tendency in online reviews in a timely manner. Therefore, a deep learning-based real online consumer sentiment (...)
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  42. Quantum Deep Learning Triuniverse.Angus McCoss - 2016 - Journal of Quantum Information Science 6 (4).
    An original quantum foundations concept of a deep learning computational Universe is introduced. The fundamental information of the Universe (or Triuniverse)is postulated to evolve about itself in a Red, Green and Blue (RGB) tricoloured stable self-mutuality in three information processing loops. The colour is a non-optical information label. The information processing loops form a feedback-reinforced deep learning macrocycle with trefoil knot topology. Fundamental information processing is driven by ψ-Epistemic Drive, the Natural appetite for information selected for (...)
     
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  43. Deep Learning-Based Artistic Inheritance and Cultural Emotion Color Dissemination of Qin Opera.Han Yu - 2022 - Frontiers in Psychology 13.
    How to enable the computer to accurately analyze the emotional information and story background of characters in Qin opera is a problem that needs to be studied. To promote the artistic inheritance and cultural emotion color dissemination of Qin opera, an emotion analysis model of Qin opera based on attention residual network is presented. The neural network is improved and optimized from the perspective of the model, learning rate, network layers, and the network itself, and then multi-head attention is (...)
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  44. Deep Learning in a Disorienting World.Jon F. Wergin - 2019 - Cambridge University Press.
    Much has been written about the escalating intolerance of worldviews other than one's own. Reasoned arguments based on facts and data seem to have little impact in our increasingly post-truth culture dominated by social media, fake news, tribalism, and identity politics. Recent advances in the study of human cognition, however, offer insights on how to counter these troubling social trends. In this book, psychologist Jon F. Wergin calls upon recent research in learning theory, social psychology, politics, and the arts (...)
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  45.  8
    Semiparametric Deep Learning Manipulator Inverse Dynamics Modeling Method for Smart City and Industrial Applications.Nan Liu, Liangyu Li, Bing Hao, Liusong Yang, Tonghai Hu, Tao Xue, Shoujun Wang & Xingmao Shao - 2020 - Complexity 2020:1-11.
    In smart cities and factories, robotic applications require high accuracy and security, which depends on precise inverse dynamics modeling. However, the physical modeling methods cannot include the nondeterministic factors of the manipulator, such as flexibility, joint clearance, and friction. In this paper, the Semiparametric Deep Learning method is proposed to model robot inverse dynamics. SDL is a type of deep learning framework, designed for optimal inference, combining the Rigid Body Dynamics model and Nonparametric Deep (...) model. The SDL model takes advantage of the global characteristics of classic RBD and the powerful fitting capabilities of the deep learning approach. Moreover, the parametric and nonparametric parts of the SDL model can be optimized at the same time instead of being optimized separately. The proposed method is validated using experiments, performed on a UR5 robotic platform. The results show that the performance of SDL model is better than that of RBD model and NDL model. SDL can always provide relatively accurate joint torque prediction, even when the RBD or NDL model is not accurate. (shrink)
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  46.  12
    Deep Learning-Based Language Identification in English-Hindi-Bengali Code-Mixed Social Media Corpora.Anupam Jamatia, Amitava Das & Björn Gambäck - 2019 - Journal of Intelligent Systems 28 (3):399-408.
    This article addresses language identification at the word level in Indian social media corpora taken from Facebook, Twitter and WhatsApp posts that exhibit code-mixing between English-Hindi, English-Bengali, as well as a blend of both language pairs. Code-mixing is a fusion of multiple languages previously mainly associated with spoken language, but which social media users also deploy when communicating in ways that tend to be rather casual. The coarse nature of code-mixed social media text makes language identification challenging. Here, the performance (...)
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  47.  9
    DLI: A Deep Learning-Based Granger Causality Inference.Wei Peng - 2020 - Complexity 2020:1-6.
    Integrating autoencoder, long short-term memory, and convolutional neural network, we propose an interpretable deep learning architecture for Granger causality inference, named deep learning-based Granger causality inference. Two contributions of the proposed DLI are to reveal the Granger causality between the bitcoin price and S&P index and to forecast the bitcoin price and S&P index with a higher accuracy. Experimental results demonstrate that there is a bidirectional but asymmetric Granger causality between the bitcoin price and S&P index. (...)
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  48. Students’ Entire Deep Learning Personality Model and Perceived Teachers’ Emotional Support.Enyun Liu, Jingxian Zhao & Noorzareith Sofeia - 2022 - Frontiers in Psychology 12.
    In recent years, deep learning as the requirement of higher education for students has attracted the attention of many scholars, and previous studies focused on defining deep learning as the deep processing of knowledge of the brain, however, in the process of knowledge processing, the brain not only involves the deep processing of information but also participates in learning consciously and emotionally. Therefore, this research proposed a four-factor model hypothesis for deep (...) that includes deep learning investment, deep cognitive-emotional experience, deep information processing, and deep learning meta-cognitive. In addition, the research proposed teachers’ emotional support perceived by students has an effect on the four factors of deep learning. Through SPSS 26 and AMOS 24, this research has verified the four-factor model of deep learning applying exploratory factor analysis and confirmatory factor analysis and verified that the perceived teacher emotional support has an impact on the four factors of students’ deep learning using the SEM. (shrink)
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  49.  4
    Beyond Human: Deep Learning, Explainability and Representation.M. Beatrice Fazi - forthcoming - Theory, Culture and Society:026327642096638.
    This article addresses computational procedures that are no longer constrained by human modes of representation and considers how these procedures could be philosophically understood in terms of ‘algorithmic thought’. Research in deep learning is its case study. This artificial intelligence technique operates in computational ways that are often opaque. Such a black-box character demands rethinking the abstractive operations of deep learning. The article does so by entering debates about explainability in AI and assessing how technoscience and (...)
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    A Deep-Learning Method for Latent Space Analysis of Multiple Seismic Attributes.Bradley C. Wallet & Thang N. Ha - 2021 - Interpretation 9 (3):T945-T954.
    Seismic attributes are a well-established method for highlighting subtle features in seismic data to improve interpretability and suitability for quantitative analysis. Seismic attributes are an enabling technology in such areas as thin-bed analysis, geobody extraction, and seismic geomorphology. Seismic attributes are mathematical functions of the data that are designed to exploit geologic and/or geophysical principles to provide meaningful information about underlying processes. Seismic attributes often suffer from an “abundance of riches” because the high dimensionality of seismic attributes may cause great (...)
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