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      Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach

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          Deep Residual Learning for Image Recognition

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            Gradient-based learning applied to document recognition

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              Representation learning: a review and new perspectives.

              The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.
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                Author and article information

                Contributors
                Journal
                IEEE Journal on Selected Areas in Communications
                IEEE J. Select. Areas Commun.
                Institute of Electrical and Electronics Engineers (IEEE)
                0733-8716
                1558-0008
                January 2022
                January 2022
                : 40
                : 1
                : 197-211
                Article
                10.1109/JSAC.2021.3126087
                33ec633e-920d-4ec5-9132-9f31230c7f31
                © 2022

                https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-037

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