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Machine Translation (I.2.7...)
Machine learning for data streams: with practical examples in MOA
Bifet A., Gavaldà R., Holmes G., Pfahringer B., The MIT Press, Cambridge, MA, 2017. 288 pp. Type: Book (978-0-262037-79-2)
Data streams are everywhere. Sensors, people, and several applications continuously produce data streams, that is, related data items in temporal order, like the ones in financial markets or social media. They are also referred to as big data due ...
Dec 13 2018
Making machine learning robust against adversarial inputs
Goodfellow I., McDaniel P., Papernot N. Communications of the ACM 61(7): 56-66, 2018. Type: Article
Machine learning (ML) has become ubiquitous in recent times. It is used in numerous (important) applications and its use will seemingly only increase. The current ML state of the art can be attributed to “nearly 50 years of research and deve...
Oct 15 2018
Poibeau T., The MIT Press, Cambridge, MA, 2017. 296 pp. Type: Book (978-0-262534-21-5), Reviews: (3 of 3)
Linguistic competence is a specific skill that some exceptionally gifted people possess when learning a foreign language by listening to a native speaker or reading a text written by a native speaker. The comprehension of an utterance comes from a...
Sep 28 2018
Learning safe multi-label prediction for weakly labeled data
Wei T., Guo L., Li Y., Gao W. Machine Learning 107(4): 703-725, 2018. Type: Article
Many real-world applications involve learning in the presence of multiple labels. For example, in the case of images, a single image may be labeled sky, cloud, or even flower. To make matters more complicated, the dataset for training may have mis...
Sep 11 2018
When simpler data does not imply less information: a study of user profiling scenarios with constrained view of mobile HTTP(S) traffic
Park S., Matic A., Garg K., Oliver N. ACM Transactions on the Web 12(2): 1-23, 2018. Type: Article
This well-written paper reports on a study revealing the power of limited datasets to characterize mobile device users. This excellent work deserves attention both for the results and for the general explanations of technologies and use of data, a...
Aug 29 2018
Poibeau T., The MIT Press, Cambridge, MA, 2017. 296 pp. Type: Book (978-0-262534-21-5), Reviews: (2 of 3)
This small book (approximately size A5), part of the Massachusetts Institute of Technology (MIT) Press “Essential Knowledge” series, captures the essence of the machine translation (MT) field, from its origins to the present. As opposed...
Aug 20 2018
Mitchell T., Cohen W., Hruschka E., Talukdar P., Yang B., Betteridge J., Carlson A., Dalvi B., Gardner M., Kisiel B., Krishnamurthy J., Lao N., Mazaitis K., Mohamed T., Nakashole N., Platanios E., Ritter A., Samadi M., Settles B., Wang R., Wijaya D., Gupta A., Chen X., Saparov A., Greaves M., Welling J. Communications of the ACM 61(5): 103-115, 2018. Type: Article
Human beings are continuously learning via exposure to a variety of experiences and situations. Can current computers with limited knowledge bases (KBs) and learning models ever mimic real human intelligence? Mitchell et al. compellingly critique ...
Aug 17 2018
Poibeau T., The MIT Press, Cambridge, MA, 2017. 296 pp. Type: Book (978-0-262534-21-5), Reviews: (1 of 3)
, by Thierry Poibeau, is a pocket-sized text. Although not technical, the book provides a good general overview of the subject. Machine translation is a wide and very practical subject so a general text explaining basic c...
Feb 7 2018
Hybrid approaches to machine translation
Costa-jussà M., Rapp R., Lambert P., Eberle K., Banchs R., Babych B., Springer International Publishing, New York, NY, 2016. 205 pp. Type: Book (978-3-319213-10-1)
Hybrid approaches to machine translation
is a book devoted to an extremely interesting and useful topic. The book presents recent research conducted by linguists and practitioners from different multidisciplinary areas in emerging computer ...
Jan 27 2017
Bhattacharyya P., Chapman & Hall/CRC, Boca Raton, FL, 2015. 248 pp. Type: Book (978-1-439897-18-8)
Machine translation (MT) was one of the earliest goals of artificial intelligence (AI) research; its first rise in the late 1950s and fall in the mid-1960s is one of the classic examples of excessive hype followed by dramatic deflation in the hist...
Aug 25 2015
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