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 Article
Gated-Attention Architectures for Task-Oriented Language Grounding
Devendra Singh Chaplot,Kanthashree Mysore Sathyendra,Rama Kumar Pasumarthi,Dheeraj Rajagopal,Ruslan Salakhutdinov +4 more
TL;DR: An end-to-end trainable neural architecture for task-oriented language grounding in 3D environments which assumes no prior linguistic or perceptual knowledge and requires only raw pixels from the environment and the natural language instruction as input.
Posted Content
Review Networks for Caption Generation
TL;DR: The review network performs a number of review steps with attention mechanism on the encoder hidden states, and outputs a thought vector after each review step; the thought vectors are used as the input of the attention mechanism in the decoder.
Posted Content
Open Domain Question Answering Using Early Fusion of Knowledge Bases and Text.
Haitian Sun,Bhuwan Dhingra,Manzil Zaheer,Kathryn Mazaitis,Ruslan Salakhutdinov,William W. Cohen +5 more
TL;DR: A novel model is proposed, GRAFT-Net, for extracting answers from a question-specific subgraph containing text and Knowledge Bases entities and relations that is competitive with the state-of-the-art when tested using either KBs or text alone, and vastly outperforms existing methods in the combined setting.
Journal ArticleDOI
Discovering binary codes for documents by learning deep generative models.
TL;DR: A deep generative model in which the lowest layer represents the word-count vector of a document and the top layer represents a learned binary code for that document is described, which allows more accurate and much faster retrieval than latent semantic analysis.
Proceedings Article
Learning in Markov Random Fields using Tempered Transitions
TL;DR: This paper shows that using MCMC operators based on tempered transitions enables the stochastic approximation algorithm to better explore highly multimodal distributions, which considerably improves parameter estimates in large, densely-connected MRF's.