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...

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Bibliographische Detailangaben
Format:	Online
Sprache:	Englisch
Veröffentlicht:	MDPI - Multidisciplinary Digital Publishing Institute 2025
Schlagworte:	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
Online-Zugang:	ONIX_20250220_9783725829828_514
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Information-Theoretic Methods in Deep Learning

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