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William G. Macready
Researcher at D-Wave Systems
Publications - 93
Citations - 15788
William G. Macready is an academic researcher from D-Wave Systems. The author has contributed to research in topics: Quantum computer & Optimization problem. The author has an hindex of 34, co-authored 91 publications receiving 13024 citations. Previous affiliations of William G. Macready include IBM & Santa Fe Institute.
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Optimal Search on a Technology Landscape
TL;DR: In this article, a "technology landscape" is introduced, where the optimal strategy is to assign a reservation price to each possible technology and the search is modeled as movement, constrained by the cost of innovation, over the technology landscape.
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An Efficient Method to Estimate Bagging's Generalization Error
TL;DR: In this paper, the bias-variance decomposition is used to estimate the generalization error of a bagged learning algorithm without invoking yet more training of the underlying learning algorithm.
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Undirected Graphical Models as Approximate Posteriors
TL;DR: An efficient method to train undirected approximate posteriors is developed by showing that the gradient of the training objective with respect to the parameters of the Undirected posterior can be computed by backpropagation through Markov chain Monte Carlo updates.
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DVAE#: Discrete Variational Autoencoders with Relaxed Boltzmann Priors
TL;DR: The authors relax Boltzmann machines to continuous distributions that permit training with importance-weighted bounds, based on generalized overlapping transformations and the Gaussian integral trick, and they show that these relaxations outperform previous discrete VAEs with Boltzman priors.
Patent
A method for optimal search on a technology landscape
TL;DR: In this paper, the authors model the search process of a firm's search for technological improvements as a Markov random field, and show that early in the search, if the initial position is poor or average, it is optimal to search far away on the technology landscape; but as the firm succeeds in finding technological improvements, and confine search to a local region of the landscape, the optimal strategy is to assign a reservation price to each possible technology.