The new model is based upon swung NURBS surfaces and it inherits their desirable crosssectional design properties It melds these geometric features with the demonstrated conveniences of surface design within a physicsbased framework We demonstrate s ID: 4571 Download Pdf
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
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 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
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 Abstract Dynamic textured sequences are characterized by the intera ctions be tween many particles or objects in the scene Based on earlie r work the im ages of the sequence are interpreted as the output of a linear autoregressive process
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
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
Without using pretraining we obtain results superior to those reported by Hinton Salakhutdinov 2006 on the same tasks they considered Our method is practical easy to use scales nicely to very large datasets and isnt limited in applicability to aut
Without using pretraining we obtain results superior to those reported by Hinton Salakhutdinov 2006 on the same tasks they considered Our method is practical easy to use scales nicely to very large datasets and isnt limited in applicability to aut
torontoedu Eugene Fiume Abstract Understanding how to create the right movement for a speci64257c character is a signi64257cant open problem in computer animation This same problem however has been at the heart of the work of performance artists for
