![\"dates\"](\"http:\/\/www.ceessnoek.info\/wp-content\/uploads\/2016\/11\/dates-e1480424879168.png\")
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We are witnessing a revolution in machine learning with the reinvigorated usage of neural networks in deep learning, which\u00a0promises a solution to cognitive tasks that are easy for humans to perform but hard to describe formally. It is intended to allow\u00a0computers to acquire knowledge directly from data without the need for human to specify, and model the inherent problem in terms\u00a0of a layered composition of simpler concepts making it possible to express complex problems by elementary operators. By not\u00a0relying on handcrafted features, hard-coded knowledge and showing the ability to regress intricate objective functions, deep\u00a0learning methods are now employed in a broad spectrum of applications from image classification to speech recognition. Deep\u00a0learning achieves exceptional power and flexibility by learning to represent the task as a nested hierarchy of layers, with more\u00a0abstract representations computed in terms of less abstract ones. The current resurgence is a result of the breakthroughs in efficient\u00a0layer-wise training, availability of big datasets, and faster computers. Thanks to the simplified training of very deep architectures,\u00a0today we can provide these algorithms with the resources they need to succeed.
\nA number of challenges are being raised and pursued. For instance, many deep learning algorithms have been designed to tackle\u00a0supervised learning problem for a wide variety of tasks, and how to reliably solve unsupervised learning problems in a similar\u00a0degree of success is an important issue to address. Another key research area is to work successfully with smaller datasets, focusing\u00a0on how we can take advantage of large quantities of unlabeled examples with a few labeled samples. Deep agents may play a more\u00a0significant role in hybrid decision systems where other machine learning techniques are used to address the reasoning, bridging the\u00a0gap between data and application decisions. We expect deep learning to be applied to increasingly multi-modal problems with more\u00a0structure in the data, opening application domains in robotics and data mining.
\nThis special issue in the high-impact IEEE Signal Processing Magazine seeks to provide a venue accessible to a wide and diverse\u00a0audience to survey the recent research R&D advances in learning, including deep learning and beyond. Interested authors are asked
\nto prepare a white paper first based on the instruction and schedule outlined below.<\/p>\n
Topics of Interest include<\/strong> (but are not limited to):<\/p>\n White papers are required, and full articles are invited based on the review of white papers.<\/strong> The white paper format is up to\u00a04 pages in length, including proposed article title, motivation and significance of the topic, an outline of the proposed paper, and Guest Editors<\/strong><\/p>\n We are witnessing a revolution in machine learning with the reinvigorated usage of neural networks in deep learning, which\u00a0promises a solution to cognitive tasks that are easy for humans to perform but hard to describe formally. It is intended to allow\u00a0computers to acquire knowledge directly from data without the need for human to specify, and […]<\/p>\n","protected":false},"author":3,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3,4],"tags":[],"class_list":["post-1592","post","type-post","status-publish","format-standard","hentry","category-cfp","category-science"],"yoast_head":"\n\n
\nrepresentative references; an author list, contact information and short bios should also be included. Articles submitted to this issue\u00a0must be of tutorial and overview\/survey nature and in an accessible style to a broad audience, and have a significant relevance to
\nthe scope of the special issue. Submissions should not have been published or under review elsewhere, and should be made online\u00a0at http:\/\/mc.manuscriptcentral.com\/sps-ieee. For submission guidelines, visit http:\/\/signalprocessingsociety.org\/publicationsresources\/
\nieee-signal-processing-magazine\/information-authors-spm<\/p>\n\n