Robert Sedgewick - Robert Sedgewick
Robert Sedgewick is the founding chair and the William O. Baker Professor in the Department of Computer Science at Princeton University. He was a member of the board of directors of Adobe Systems from 1990 to 2016, served on the faculty at Brown University from 1975 to 1985, and has held visiting research positions at Xerox PARC, IDA, and INRIA. His research expertise is in algorithm science, data
Assignment 5: Tree Isomorphism
McGill University, SOCS 308-250B, Assignment 5 Winter 2002 Assignment 5: Tree Isomorphism Important Note: You do not need to completely understand all of the discussion written here to successfully complete the assignment. The discussion is meant to give you an idea of what you are doing and hopefully clear up any ambiguities in the algorithm descriptions. Send comments or questions to: msuder@cs.
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. Unfortunately, NMT systems are known to be computationally expensive both in training and in translation inference. Also, most NMT systems have difficulty with rare words. These issues have hindered NM
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The Expression Problem
