torontoedu Abstract In this paper we propose a novel method for learning a Mahalanobis distance measure to be used in the KNN classi64257cation algorithm The algorithm directly maximizes a stochastic variant of the leaveoneout KNN score on the traini ID: 7448 Download Pdf
torontoedu Geoffrey Hinton Department of Computer Science University of Toronto Toronto Ontario M5S 3G4 hintoncstorontoedu ABSTRACT We show how to learn a deep graphical model of the wordcount vectors obtained from a large set of documents The values
torontoedu Geoffrey Hinton Department of Computer Science University of Toronto hintoncstorontoedu Abstract We present a new learning algorithm for Boltz mann machines that contain many layers of hid den variables Datadependent expectations are estim
tangcstorontoedu Ruslan Salakhutdinov Department of Computer Science and Statistics University of Toronto Toronto Ontario Canada rsalakhucstorontoedu Abstract Multilayer perceptrons MLPs or neural networks are popular models used for nonlinear regre
torontoedu Ruslan Salakhutdinov Department of Statistics and Computer Science University of Toronto rsalakhucstorontoedu Abstract A Deep Boltzmann Machine is described for learning a generative model of data that consists of multiple and diverse inpu
torontoedu Geoffrey Hinton Department of Computer Science University of Toronto hintoncstorontoedu Abstract We present a new learning algorithm for Boltz mann machines that contain many layers of hid den variables Datadependent expectations are estim
torontoedu Abstract Attention has long been proposed by psychologists to be important for ef64257ciently dealing with the massive amounts of sensory stimulus in the neocortex Inspired by the attention models in visual neuroscience and the need for ob
torontoedu Department of Computer Science University of Toronto Toronto Ontario M5S 3G4 Canada Geo64256rey Hinton Abstract We introduce a type of Deep Boltzmann Ma chine DBM that is suitable for extracting distributed semantic representations from a
torontoedu Abstract Many existing approaches to collaborative 64257ltering can neither handle very large datasets nor easily deal with users who have very few ratings In this paper we present the Probabilistic Matrix Factorization PMF model which sca
torontoedu Abstract This is a note to explain Fisher linear discriminant analysis 1 Fisher LDA The most famous example of dimensionality reduction is principal components analysis This technique searches for directions in the data that have largest v
Toronto ON M5S 3G4 CANADA Abstract Recurrent Neural Networks RNNs are very powerful sequence models that do not enjoy widespread use because it is extremely dif64257 cult to train them properly Fortunately re cent advances in Hessianfree optimizatio
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torontoedu Abstract In this paper we propose a novel method for learning a Mahalanobis distance measure to be used in the KNN classi64257cation algorithm The algorithm directly maximizes a stochastic variant of the leaveoneout KNN score on the traini
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