On the Computational Power of Neural Nets
TLDR
It is proved that one may simulate all Turing machines by such nets, and any multi-stack Turing machine in real time, and there is a net made up of 886 processors which computes a universal partial-recursive function.About:
This article is published in Journal of Computer and System Sciences.The article was published on 1995-02-01 and is currently open access. It has received 837 citations till now. The article focuses on the topics: Super-recursive algorithm & Universal Turing machine.read more
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Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
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On the difficulty of training Recurrent Neural Networks
TL;DR: This paper proposes a gradient norm clipping strategy to deal with exploding gradients and a soft constraint for the vanishing gradients problem and validates empirically the hypothesis and proposed solutions.
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The empirical case for two systems of reasoning.
TL;DR: The distinction between rule-based and associative systems of reasoning has been discussed extensively in cognitive psychology as discussed by the authors, where the distinction is based on the properties that are normally assigned to rules.
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An Introduction to Deep Learning for the Physical Layer
Timothy J. O'Shea,Jakob Hoydis +1 more
TL;DR: In this article, an end-to-end reconstruction task was proposed to jointly optimize transmitter and receiver components in a single process, which can be extended to networks of multiple transmitters and receivers.
References
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A logical calculus of the ideas immanent in nervous activity
Warren S. McCulloch,Walter Pitts +1 more
TL;DR: In this article, it is shown that many particular choices among possible neurophysiological assumptions are equivalent, in the sense that for every net behaving under one assumption, there exists another net which behaves under another and gives the same results, although perhaps not in the same time.
Book
Introduction to Automata Theory, Languages, and Computation
TL;DR: This book is a rigorous exposition of formal languages and models of computation, with an introduction to computational complexity, appropriate for upper-level computer science undergraduates who are comfortable with mathematical arguments.
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Finding Structure in Time
TL;DR: A proposal along these lines first described by Jordan (1986) which involves the use of recurrent links in order to provide networks with a dynamic memory and suggests a method for representing lexical categories and the type/token distinction is developed.
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An introduction to computing with neural nets
TL;DR: This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification and exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components.
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Neural computation of decisions in optimization problems
John J. Hopfield,David W. Tank +1 more
TL;DR: Results of computer simulations of a network designed to solve a difficult but well-defined optimization problem-the Traveling-Salesman Problem-are presented and used to illustrate the computational power of the networks.