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Almost Optimal Lower Bounds for Small Depth Circuits.

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The article was published on 1989-01-01 and is currently open access. It has received 662 citations till now. The article focuses on the topics: Upper and lower bounds.

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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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Representation Learning: A Review and New Perspectives

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TL;DR: The motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer modelssuch as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks are discussed.
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The Probabilistic Method

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TL;DR: A particular set of problems - all dealing with “good” colorings of an underlying set of points relative to a given family of sets - is explored.

Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising 1 criterion

P. Vincent
TL;DR: This work clearly establishes the value of using a denoising criterion as a tractable unsupervised objective to guide the learning of useful higher level representations.
References
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Proceedings ArticleDOI

Parity, circuits, and the polynomial-time hierarchy

TL;DR: A super-polynomial lower bound is given for the size of circuits of fixed depth computing the parity function and connections are given to the theory of programmable logic arrays and to the relativization of the polynomial-time hierarchy.
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Computational limitations of small-depth circuits

Johan Håstad
TL;DR: The techniques described in "Computational Limitations for Small Depth Circuits" can be used to demonstrate almost optimal lower bounds on the size of small depth circuits computing several different functions, such as parity and majority.
Proceedings ArticleDOI

Separating the polynomial-time hierarchy by oracles

TL;DR: In this paper, the size of depth-k Boolean circuits for computing certain functions is shown to be polynomial in the number of levels in the hierarchy of the hierarchy, i.e., ΣkP,A is properly contained in Σp+1P+A for all k.
Journal ArticleDOI

The monotone circuit complexity of Boolean functions

TL;DR: The arguments of Razborov are modified to obtain exponential lower bounds for circuits, and the best lower bound for an NP function ofn variables is exp (Ω(n1/4 · (logn)1/2)), improving a recent result of exp ( Ω( n1/8-ε)) due to Andreev.