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.
Papers
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Proceedings ArticleDOI
Learning Robust Visual-Semantic Embeddings
TL;DR: An end-to-end learning framework that is able to extract more robust multi-modal representations across domains and a novel technique of unsupervised-data adaptation inference is introduced to construct more comprehensive embeddings for both labeled and unlabeled data.
Posted Content
Path-SGD: Path-Normalized Optimization in Deep Neural Networks
TL;DR: This work revisits the choice of SGD for training deep neural networks by reconsidering the appropriate geometry in which to optimize the weights, and suggests Path-SGD, which is an approximate steepest descent method with respect to a path-wise regularizer related to max-norm regularization.
Journal ArticleDOI
Restricted Boltzmann Machines for Neuroimaging: an Application in Identifying Intrinsic Networks
R Devon Hjelm,Vince D. Calhoun,Ruslan Salakhutdinov,Elena A. Allen,Tulay Adali,Sergey M. Plis +5 more
TL;DR: It is shown that RBMs can be used to identify networks and their temporal activations with accuracy that is equal or greater than that of factorization models, a significant prospect for future neuroimaging research.
Proceedings Article
Transfer Learning by Borrowing Examples for Multiclass Object Detection
TL;DR: This work proposes a novel way of augmenting the training data for each class by borrowing and transforming examples from other classes, and demonstrates that the new object detector improves upon the current state-of-the-art detector on the challenging SUN09 object detection dataset.
Proceedings Article
Learning Stochastic Feedforward Neural Networks
TL;DR: A stochastic feedforward network with hidden layers composed of both deterministic and stochastics variables is proposed that achieves superior performance on synthetic and facial expressions datasets compared to conditional Restricted Boltzmann Machines and Mixture Density Networks.