Automatic Differentiation of Algorithms for Machine Learning

Automatic differentiation---the mechanical transformation of numeric computer programs to calculate derivatives efficiently and accurately---dates to the origin of the computer age. Reverse mode automatic differentiation both antedates and generalizes the method of backwards propagation of errors used in machine learning. Despite this, practitioners in a variety of fields, including machine learning, have been little influenced by automatic differentiation, and make scant use of available tools. Here we review the technique of automatic differentiation, describe its two main modes, and explain how it can benefit machine learning practitioners. To reach the widest possible audience our treatment assumes only elementary differential calculus, and does not assume any knowledge of linear algebra.Comment: 7 pages, 1 figur

Technology networks for socially useful production

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Cold Start for the Green Innovation Machine

This Policy Contribution accompanies the Policy Brief, Â?No Green Growth Without InnovationÂ?. Written by Senior Non-Resident Fellow Philippe Aghion, Senior Resident Fellow Reinhilde Veugelers and Researcher Clément Serre, this paper discusses the state of green innovation and goes into more depth in discussing the current problems in the area. Examining research and development, patent, and venture capital data, the authors point out that there is momentum for private investment in green technologies. However, they argue that, thus far, the implicit tax rate on energy in the EU27 is too low and fragmented, the carbon price in the EU Emissions Trading System is too volatile, and the public R&D expenditures dedicated to energy and environment are too low. They conclude that immediate state intervention is necessary, at least at the onset, to ensure that the Â?green innovation machineÂ? gets properly started.

The scientific evaluation of music content analysis systems: Valid empirical foundations for future real-world impact

We discuss the problem of music content analysis within the formal framework of experimental design

Machine translation evaluation resources and methods: a survey

We introduce the Machine Translation (MT) evaluation survey that contains both manual and automatic evaluation methods. The traditional human evaluation criteria mainly include the intelligibility, fidelity, fluency, adequacy, comprehension, and informativeness. The advanced human assessments include task-oriented measures, post-editing, segment ranking, and extended criteriea, etc. We classify the automatic evaluation methods into two categories, including lexical similarity scenario and linguistic features application. The lexical similarity methods contain edit distance, precision, recall, F-measure, and word order. The linguistic features can be divided into syntactic features and semantic features respectively. The syntactic features include part of speech tag, phrase types and sentence structures, and the semantic features include named entity, synonyms, textual entailment, paraphrase, semantic roles, and language models. The deep learning models for evaluation are very newly proposed. Subsequently, we also introduce the evaluation methods for MT evaluation including different correlation scores, and the recent quality estimation (QE) tasks for MT. This paper differs from the existing works\cite {GALEprogram2009, EuroMatrixProject2007} from several aspects, by introducing some recent development of MT evaluation measures, the different classifications from manual to automatic evaluation measures, the introduction of recent QE tasks of MT, and the concise construction of the content