IBM Journal of Research and Development

Volume 61 Issue 4/5 • July-Sept. 2017

Deep Learning
Many recent breakthroughs in areas ranging from speech and image recognition to game playing, autonomous vehicles, and medical diagnostics have been made possible by artificial intelligence techniques popularly known as deep learning. Deep learning is a branch of machine learning that usually makes use of multiple processing layers and hierarchical representations to drive the learning process. This issue of the IBM Journal of Research and Development emphasizes new applications, architectures, data sets, tools, and other technologies that advance the field in deep learning. In particular, this issue covers various practical applications (e.g., involving speech, language, and image recognition), deep learning accelerators, hyperparameter optimization and parameter servers, and deep learning capabilities provided as a service.
Three nontopical papers at the end of this issue concern machine learning and hardware design, natural-language classifiers for question-answering systems, and speech recognition approaches for U.S. Open Tennis Championships.
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Aims & Scope

The IBM Journal of Research and Development is a peer-reviewed technical journal, published bimonthly, which features the work of authors in the science, technology and engineering of information systems.


The following IBM journal articles are freely available for all users to view:



Algorithmic Information Theory
Functional Dependencies in a Relational Database and Propositional Logic
Notes on the history of reversible computation
SEQUEL 2: A Unified Approach to Data Definition, Manipulation, and Control
Some Studies in Machine Learning Using the Game of Checkers
The Design of APL
The evolution of RISC technology at IBM
The Experimental Compiling System



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Rachel D'Annucci Henriquez
IBM T. J. Watson Research Center