Information-Theoretic Methods in Deep Learning
The rapid development of deep learning has led to groundbreaking advancements across various fields, from computer vision to natural language processing and beyond. Information theory, as a mathematical foundation for understanding data representation, learning, and communication, has emerged as a p...
Furkejuvvon:
| Materiálatiipa: | Online |
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| Giella: | eaŋgalasgiella |
| Almmustuhtton: |
MDPI - Multidisciplinary Digital Publishing Institute
2025
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| Fáttát: | |
| Liŋkkat: | ONIX_20250220_9783725829828_514 |
| Fáddágilkorat: |
Eai fáddágilkorat, Lasit vuosttaš fáddágilkora!
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| _version_ | 1869524854039904256 |
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| collection | Directory of Open Access Books |
| description | The rapid development of deep learning has led to groundbreaking advancements across various fields, from computer vision to natural language processing and beyond. Information theory, as a mathematical foundation for understanding data representation, learning, and communication, has emerged as a powerful tool in advancing deep learning methods.This Special Issue, "Information-Theoretic Methods in Deep Learning: Theory and Applications", presents cutting-edge research that bridges the gap between information theory and deep learning. It covers theoretical developments, innovative methodologies, and practical applications, offering new insights into the optimization, generalization, and interpretability of deep learning models. The collection includes contributions on: Theoretical frameworks combining information theory with deep learning architectures; Entropy-based and information bottleneck methods for model compression and generalization; Mutual information estimation for feature selection and representation learning; Applications of information-theoretic principles in natural language processing, computer vision, and neural network optimization. |
| format | Online |
| id | doab-20.500.12854ir-153150 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1531502025-02-20T13:38:25Z Information-Theoretic Methods in Deep Learning Yang, Shuangming Yu, Shujian Sánchez Giraldo, Luis Gonzalo Chen, Badong sentiment analysis deep neural networks convolutional neural network ResNet Res2Net multiple time series forecasting method information bottleneck entropy KL-divergence mutual information deep models RNN U-Net partial convolutions information theory deep learning information plane kernels methods deep neural network generalization ability regularization method continual learning quadruped robot locomotion reinforcement learning plasticity active learning universal prediction deep active learning individual sequences normalized maximum likelihood out-of-distribution few-shot learning meta learning graph semi-supervision label propagation Gaussian kernel function D-S evidence theory self-supervised learning representation learning generative adversarial networks parameterized loss functions f-divergence Jensen-f-divergence time series modelling forecasting nonlinear modelling denoising anomaly detection degradation modelling machine learning thema EDItEUR::M Medicine and Nursing::MJ Clinical and internal medicine::MJC Diseases and disorders::MJCL Oncology thema EDItEUR::P Mathematics and Science The rapid development of deep learning has led to groundbreaking advancements across various fields, from computer vision to natural language processing and beyond. Information theory, as a mathematical foundation for understanding data representation, learning, and communication, has emerged as a powerful tool in advancing deep learning methods.This Special Issue, "Information-Theoretic Methods in Deep Learning: Theory and Applications", presents cutting-edge research that bridges the gap between information theory and deep learning. It covers theoretical developments, innovative methodologies, and practical applications, offering new insights into the optimization, generalization, and interpretability of deep learning models. The collection includes contributions on: Theoretical frameworks combining information theory with deep learning architectures; Entropy-based and information bottleneck methods for model compression and generalization; Mutual information estimation for feature selection and representation learning; Applications of information-theoretic principles in natural language processing, computer vision, and neural network optimization. 2025-02-20T13:38:22Z 2025-02-20T13:38:22Z 2025 book ONIX_20250220_9783725829828_514 9783725829828 9783725829811 https://directory.doabooks.org/handle/20.500.12854/153150 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/10425 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-2981-1 10.3390/books978-3-7258-2981-1 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725829828 9783725829811 244 Basel open access |
| spellingShingle | sentiment analysis deep neural networks convolutional neural network ResNet Res2Net multiple time series forecasting method information bottleneck entropy KL-divergence mutual information deep models RNN U-Net partial convolutions information theory deep learning information plane kernels methods deep neural network generalization ability regularization method continual learning quadruped robot locomotion reinforcement learning plasticity active learning universal prediction deep active learning individual sequences normalized maximum likelihood out-of-distribution few-shot learning meta learning graph semi-supervision label propagation Gaussian kernel function D-S evidence theory self-supervised learning representation learning generative adversarial networks parameterized loss functions f-divergence Jensen-f-divergence time series modelling forecasting nonlinear modelling denoising anomaly detection degradation modelling machine learning thema EDItEUR::M Medicine and Nursing::MJ Clinical and internal medicine::MJC Diseases and disorders::MJCL Oncology thema EDItEUR::P Mathematics and Science Information-Theoretic Methods in Deep Learning |
| title | Information-Theoretic Methods in Deep Learning |
| title_full | Information-Theoretic Methods in Deep Learning |
| title_fullStr | Information-Theoretic Methods in Deep Learning |
| title_full_unstemmed | Information-Theoretic Methods in Deep Learning |
| title_short | Information-Theoretic Methods in Deep Learning |
| title_sort | information theoretic methods in deep learning |
| topic | sentiment analysis deep neural networks convolutional neural network ResNet Res2Net multiple time series forecasting method information bottleneck entropy KL-divergence mutual information deep models RNN U-Net partial convolutions information theory deep learning information plane kernels methods deep neural network generalization ability regularization method continual learning quadruped robot locomotion reinforcement learning plasticity active learning universal prediction deep active learning individual sequences normalized maximum likelihood out-of-distribution few-shot learning meta learning graph semi-supervision label propagation Gaussian kernel function D-S evidence theory self-supervised learning representation learning generative adversarial networks parameterized loss functions f-divergence Jensen-f-divergence time series modelling forecasting nonlinear modelling denoising anomaly detection degradation modelling machine learning thema EDItEUR::M Medicine and Nursing::MJ Clinical and internal medicine::MJC Diseases and disorders::MJCL Oncology thema EDItEUR::P Mathematics and Science |
| topic_facet | sentiment analysis deep neural networks convolutional neural network ResNet Res2Net multiple time series forecasting method information bottleneck entropy KL-divergence mutual information deep models RNN U-Net partial convolutions information theory deep learning information plane kernels methods deep neural network generalization ability regularization method continual learning quadruped robot locomotion reinforcement learning plasticity active learning universal prediction deep active learning individual sequences normalized maximum likelihood out-of-distribution few-shot learning meta learning graph semi-supervision label propagation Gaussian kernel function D-S evidence theory self-supervised learning representation learning generative adversarial networks parameterized loss functions f-divergence Jensen-f-divergence time series modelling forecasting nonlinear modelling denoising anomaly detection degradation modelling machine learning thema EDItEUR::M Medicine and Nursing::MJ Clinical and internal medicine::MJC Diseases and disorders::MJCL Oncology thema EDItEUR::P Mathematics and Science |
| url | ONIX_20250220_9783725829828_514 |