R
Ruslan Salakhutdinov
Researcher at Carnegie Mellon University
Publications - 457
Citations - 142495
Ruslan Salakhutdinov is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 107, co-authored 410 publications receiving 115921 citations. Previous affiliations of Ruslan Salakhutdinov include Carnegie Learning & University of Toronto.
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Proceedings ArticleDOI
Transformer-XL: Attentive Language Models beyond a Fixed-Length Context.
TL;DR: This work proposes a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence, which consists of a segment-level recurrence mechanism and a novel positional encoding scheme.
Proceedings Article
Deep Boltzmann machines
TL;DR: A new learning algorithm for Boltzmann machines that contain many layers of hidden variables that is made more efficient by using a layer-by-layer “pre-training” phase that allows variational inference to be initialized with a single bottomup pass.
Proceedings ArticleDOI
Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books
Yukun Zhu,Ryan Kiros,Richard S. Zemel,Ruslan Salakhutdinov,Raquel Urtasun,Antonio Torralba,Sanja Fidler +6 more
TL;DR: The authors align books to their movie releases to provide rich descriptive explanations for visual content that go semantically far beyond the captions available in the current datasets, and propose a context-aware CNN to combine information from multiple sources.
Proceedings ArticleDOI
Restricted Boltzmann machines for collaborative filtering
TL;DR: This paper shows how a class of two-layer undirected graphical models, called Restricted Boltzmann Machines (RBM's), can be used to model tabular data, such as user's ratings of movies, and demonstrates that RBM's can be successfully applied to the Netflix data set.
Proceedings Article
Neighbourhood Components Analysis
TL;DR: A novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm that directly maximizes a stochastic variant of the leave-one-out KNN score on the training set.