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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.
Papers
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Proceedings Article
Improved variational autoencoders for text modeling using dilated convolutions
TL;DR: The authors showed that variational autoencoders can outperform LSTM language models when carefully managed, showing that there is a trade-off between contextual capacity of the decoder and effective use of encoding information.
Proceedings Article
Learning to Explore using Active Neural SLAM
TL;DR: This work presents a modular and hierarchical approach to learn policies for exploring 3D environments, called `Active Neural SLAM', which leverages the strengths of both classical and learning-based methods, by using analytical path planners with learned SLAM module, and global and local policies.
Proceedings Article
Multiple Futures Prediction
TL;DR: A probabilistic framework that efficiently learns latent variables to jointly model the multi-step future motions of agents in a scene and can be used for planning via computing a conditional probability density over the trajectories of other agents given a hypothetical rollout of the ego agent.
Proceedings ArticleDOI
Spatially Adaptive Computation Time for Residual Networks
Michael Figurnov,Maxwell D. Collins,Yukun Zhu,Li Zhang,Jonathan Huang,Dmitry Vetrov,Ruslan Salakhutdinov +6 more
TL;DR: Experimental results are presented showing that this model improves the computational efficiency of Residual Networks on the challenging ImageNet classification and COCO object detection datasets and the computation time maps on the visual saliency dataset cat2000 correlate surprisingly well with human eye fixation positions.
Journal ArticleDOI
Learning with Hierarchical-Deep Models
TL;DR: Efficient learning and inference algorithms for the HDP-DBM model are presented and it is shown that it is able to learn new concepts from very few examples on CIFAR-100 object recognition, handwritten character recognition, and human motion capture datasets.