Oldest Papers
Deep Learning: A Critical Appraisal
Gary Marcus
arXiv·2018
Although deep learning has historical roots going back decades, neither the term "deep learning" nor the approach was popular just over five years ago, when the field was reignited by papers such as Krizhevsky, Sutskever and Hinton's now classic (2012) deep network model of Imagenet. What has the field discovered in the five subsequent years? Against a background of considerable progress in areas such as speech recognition, image recognition, and game playing, and considerable enthusiasm in the popular press, I present ten concerns for deep learning, and suggest that deep learning must be supplemented by other techniques if we are to reach artificial general intelligence.
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View paper →Colonialism, Postcolonialism and the Liberal Welfare State
Gurminder K. Bhambra, John Holmwood
New Political Economy·2018·120 citations
This article addresses the colonial and racial origins of the welfare state with a particular emphasis on the liberal welfare state of the USA and UK. Both are understood in terms of the centrality of the commodified status of labour power expressing a logic of market relations. In contrast, we argue that with a proper understanding of the relations of capitalism and colonialism, the sale of labour power as a commodity already represents a movement away from the commodified form of labour represented by enslavement. European colonialism is integral to the development of welfare states and their forms of inclusion and exclusion which remain racialised through into the twenty-first century.
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View paper →Qualitative analysis of bibliotherapy as a tool for adults who stutter and graduate students
Unknown Author
sciencedirect.com·2018
No abstract available
★ 5.0 (1)
View paper →How Christianity Lost James Baldwin -
Douglas DeCelle
douglasdecelle.net·2018
No abstract available
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View paper →Deep Learning: An Introduction for Applied Mathematicians
Catherine F. Higham, Desmond J. Higham
arXiv·2018
Multilayered artificial neural networks are becoming a pervasive tool in a host of application fields. At the heart of this deep learning revolution are familiar concepts from applied and computational mathematics; notably, in calculus, approximation theory, optimization and linear algebra. This article provides a very brief introduction to the basic ideas that underlie deep learning from an applied mathematics perspective. Our target audience includes postgraduate and final year undergraduate students in mathematics who are keen to learn about the area. The article may also be useful for instructors in mathematics who wish to enliven their classes with references to the application of deep learning techniques. We focus on three fundamental questions: what is a deep neural network? how is a network trained? what is the stochastic gradient method? We illustrate the ideas with a short MATLAB code that sets up and trains a network. We also show the use of state-of-the art software on a large scale image classification problem. We finish with references to the current literature.
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