Google Scholar Profile
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Gated-Attention Readers for Text Comprehension
Bhuwan Dhingra, Hanxiao Liu, William W. Cohen, Ruslan Salakhutdinov
arXiv [arXiv]. -
Deep Neural Networks with Massive Learned Knowledge
Zhiting Hu, Zichao Yang, Ruslan Salakhutdinov, and Eric Xing
Conference on Empirical Methods in Natural Language Processing (EMNLP'16). -
Iterative Refinement of Approximate Posterior for Training Directed Belief Networks
Devon Hjelm, Kyunghyun Cho, Junyoung Chung, Russ Salakhutdinov, Vince Calhoun, Nebojsa Jojic
NIPS 2016, arXiv [arXiv]. -
Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations
Behnam Neyshabur, Yuhuai Wu, Ruslan Salakhutdinov, Nathan Srebro
NIPS 2016, arXiv [arXiv]. -
Stochastic Variational Deep Kernel Learning
Andrew Gordon Wilson, Zhiting Hu, Eroc Xing, Ruslan Salakhutdinov
NIPS 2016. -
On Multiplicative Integration with Recurrent Neural Networks
Yuhuai Wu, Saizheng Zhang, Ying Zhang, Yoshua Bengio, Ruslan Salakhutdinov
NIPS 2016, arXiv [arXiv]. -
Encode, Review, and Decode: Reviewer Module for Caption Generation
Zhilin Yang, Ye Yuan, Yuexin Wu, Ruslan Salakhutdinov, William W. Cohen
NIPS 2016, arXiv [arXiv]. -
Architectural Complexity Measures of Recurrent Neural Networks
Saizheng Zhang, Yuhuai Wu, Tong Che, Zhouhan Lin, Roland Memisevic, Ruslan Salakhutdinov, Yoshua Bengio
NIPS 2016, arXiv [arXiv]. -
Multi-Task Cross-Lingual Sequence Tagging from Scratch
Zhilin Yang, Ruslan Salakhutdinov, William Cohen
arXiv [arXiv]. -
Revisiting Semi-Supervised Learning with Graph Embeddings
Zhilin Yang, William Cohen, Ruslan Salakhutdinov
ICML 2016, arXiv [arXiv]. -
Importance Weighted Autoencoders
Yuri Burda, Roger Grosse, Ruslan Salakhutdinov
ICLR, 2016, [arXiv]. Code is available [here]. -
Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning
Emilio Parisotto, Jimmy Lei Ba, Ruslan Salakhutdinov
ICLR, 2016, [arXiv]. -
Generating Images from Captions with Attention
Elman Mansimov, Emilio Parisotto, Jimmy Lei Ba, Ruslan Salakhutdinov
ICLR, 2016, [arXiv], oral. [Generated Samples].- See also
Bloomberg news,
Motherboard.
- See also
Bloomberg news,
Motherboard.
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Data-Dependent Path Normalization in Neural Networks
Behnam Neyshabur, Ryota Tomioka, Ruslan Salakhutdinov, Nathan Srebro
ICLR, 2016, [arXiv]. -
Action Recognition using Visual Attention
Shikhar Sharma, Ryan Kiros, Ruslan Salakhutdinov
ICLR workshop, 2016 [arXiv]. [Code]. [Project Website]. -
Deep Kernel Learning
Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, Eric Xing
AI and Statistics, 2016, [arXiv].
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Human-level concept learning through probabilistic program induction
Brenden Lake, Ruslan Salakhutdinov, and Joshua Tenenbaum (2015),
Science, 350(6266), 1332-1338, [paper],[Supporting Info.], [visual Turing tests], [Omniglot data set], [Code].- See also New York Times, CBC, Reuters, CBS, MIT Tech Review, Toronto Star, UofT News, MIT News, Washington Post, CIFAR, Business Insider.
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Learning Wake-Sleep Recurrent Attention Models
Lei Jimmy Ba, Roger Grosse, Ruslan Salakhutdinov, Brendan Frey
NIPS 2015. [arXiv]. -
Skip-Thought Vectors
Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Antonio Torralba, Raquel Urtasun, Sanja Fidler
NIPS 2015, [arXiv]. Code is available [here]. -
Path-SGD: Path-Normalized Optimization in Deep Neural Networks
Behnam Neyshabur, Ruslan Salakhutdinov, Nathan Srebro
NIPS 2015, [arXiv]. -
Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books
Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler
ICCV 2015, [arXiv], [ project page ], oral. -
Predicting Deep Zero-Shot Convolutional Neural Networks
using Textual Descriptions
Jimmy Ba, Kevin Swersky, Sanja Fidler, Ruslan Salakhutdinov
ICCV 2015, [arXiv]. - Learning Deep Generative Models
Ruslan Salakhutdinov
Annual Review of Statistics and Its Application, Vol. 2, pp. 361–385, 2015
[pdf], 2015 -
Scaling Up Natural Gradient by Sparsely Factorizing the Inverse Fisher Matrix
Roger Grosse, Ruslan Salakhutdinov
ICML 2015, [pdf]. -
Unsupervised Learning of Video Representations using LSTMs
Nitish Srivastava, Elman Mansimov, Ruslan Salakhutdinov
ICML 2015, [arXiv], [pdf]. -
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron Courville, Ruslan Salakhutdinov, Richard Zemel, Yoshua Bengio
ICML 2015, [arXiv], [pdf], [project page].- See also
Scientific American,
CIFAR.
- See also
Scientific American,
CIFAR.
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Siamese neural networks for one-shot image recognition.
Gregory Koch, Richard Zemel, Ruslan Salakhutdinov
ICML 2015 Deep Learning Workshop (2015). [pdf]. -
Exploiting Image-trained CNN Architectures for Unconstrained Video Classification
Shengxin Zha, Florian Luisier, Walter Andrews, Nitish Srivastava, Ruslan Salakhutdinov
In BMVC 2015 [arXiv], 2015 -
segDeepM: Exploiting Segmentation and Context in Deep Neural Networks for Object Detection
Y. Zhu, R. Urtasun, R. Salakhutdinov and S.Fidler
In Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, June 2015,
[ arXiv ], [pdf], [ Project Page]. -
Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models
Ryan Kiros, Ruslan Salakhutdinov, Richard Zemel.
To appear in Transactions of the Association for Computational Linguistics (TACL), 2015.
[ arXiv], [ results], [ demo ].
An encoder-decoder architecture for ranking and generating image descriptions.
Previous version appeared in NIPS Deep Learning Workshop, 2014. -
Accurate and Conservative Estimates of MRF Log-likelihood using Reverse Annealing
Yuri Burda, Roger B. Grosse, and Ruslan Salakhutdinov
In AI and Statistics (AISTATS), 2015 [arXiv], [pdf].
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Learning Generative Models with Visual Attention
Yichuan Tang, Nitish Srivastava, and Ruslan Salakhutdinov
Neural Information Processing Systems (NIPS 28), 2014, oral.
[ pdf ], Supplementary material [ pdf]. - A Multiplicative Model for Learning Distributed Text-Based Attribute Representations
Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov.
Neural Information Processing Systems (NIPS 28), 2014
[ pdf ], Supplementary material [ zip].
Previous version appeared in ICML Workshop on Knowledge-Powered Deep Learning for Text Mining, 2014. [ arXiv]. -
Multimodal Learning with Deep Boltzmann Machines
Nitish Srivastava and Ruslan Salakhutdinov
Journal of Machine Learning Research, 2014. [ pdf ]. Code is available [ here]. -
Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan R. Salakhutdinov
Journal of Machine Learning Research, 2014. [ pdf]. -
Deep Learning for Neuroimaging: a Validation Study
S. Plis, D. Hjelm, R. Salakhutdinov, E. Allen, H. Bockholt, J. Long, H. Johnson, J. Paulsen, J. Turner, and V. Calhoun
Frontiers in Neuroscience, 2014. [ pdf]. -
Multi-task Neural Networks for QSAR Prediction
George E. Dahl, Navdeep Jaitly, Ruslan Salakhutdinov, 2014.
[ arXiv]. -
Restricted Boltzmann Machines for Neuroimaging: An Application in Identifying Intrinsic Networks
Devon Hjelma, Vince Calhouna, Ruslan Salakhutdinov, Elena Allena, Tulay Adali, and Sergey Plisa
In NeuroImage, Volume 96, Aug 1 2014, pages 245 - 260. [ pdf]. -
Multimodal Neural Language Models
Ryan Kiros, Ruslan Salakhutdinov, Richard Zemel.
In 31th International Conference on Machine Learning (ICML 2014)
[pdf], [ Project Page].
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Annealing between Distributions by Averaging Moments
Roger Grosse, Chris Maddison, and Ruslan Salakhutdinov
In Neural Information Processing Systems (NIPS 27), 2013, oral.
[pdf], Supplementary material [ pdf]. -
Discriminative Transfer Learning with Tree-based Priors
Nitish Srivastava and Ruslan Salakhutdinov
In Neural Information Processing Systems (NIPS 27), 2013, [pdf], Supplementary material [ zip]. -
Learning Stochastic Feedforward Neural Networks
Yichuan Tang and Ruslan Salakhutdinov
In Neural Information Processing Systems (NIPS 27), 2013 [pdf], Supplementary material [ pdf]. -
One-shot Learning by Inverting a Compositional Causal Process
Brenden Lake, Ruslan Salakhutdinov, and Josh Tenenbaum
In Neural Information Processing Systems (NIPS 27), 2013, [pdf], Supplementary material [ pdf]. -
The Power of Asymmetry in Binary Hashing
B. Neyshabur, N. Srebro, R. Salakhutdinov, Y. Makarychev, and P. Yadollahpour
In Neural Information Processing Systems (NIPS 27), 2013, [pdf]. -
Learning with Hierarchical-Deep Models
Ruslan Salakhutdinov, Josh Tenenbaum, and Antonio Torralba
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, pp. 1958-1971, Aug. 2013, [pdf].
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Modeling Documents with Deep Boltzmann Machines
Nitish Srivastava, Ruslan Salakhutdinov, Geoffrey Hinton
In Uncertainty in Artificial Intelligence (UAI), Seattle, USA, 2013, oral.
[pdf],
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Tensor Analyzers
Yichuan Tang, Ruslan Salakhutdinov and Geoffrey Hinton
In 30th International Conference on Machine Learning (ICML), Atlanta, USA, 2013 [pdf], [ supp ], [ code].
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Multimodal Learning with Deep Boltzmann Machines
Nitish Srivastava and Ruslan Salakhutdinov
Neural Information Processing Systems (NIPS 26), 2012, oral.
[ pdf], Supplementary material [ zip].
Code is available [ here].
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Hamming Distance Metric Learning
Mohammad Norouzi, David Fleet, and Ruslan Salakhutdinov
Neural Information Processing Systems (NIPS 26), 2012 [ pdf], Supplementary material [ pdf].
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A Better Way to Pretrain Deep Boltzmann Machines
Ruslan Salakhutdinov and Geoffrey Hinton
Neural Information Processing Systems (NIPS 26), 2012, [ pdf].
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Matrix Reconstruction with the Local Max Norm.
Rina Foygel, Nathan Srebro, Ruslan Salakhutdinov
Neural Information Processing Systems (NIPS 26), 2012, [ pdf], Supplementary material [ pdf].
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Cardinality Restricted Boltzmann Machines
Kevin Swersky, Daniel Tarlow, Ilya Sutskever, Ruslan Salakhutdinov, Richard Zemel, and Ryan Adams.
Neural Information Processing Systems (NIPS 26), 2012, [ pdf].
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An Efficient Learning Procedure for Deep Boltzmann Machines
Ruslan Salakhutdinov and Geoffrey Hinton
Neural Computation August 2012, Vol. 24, No. 8: 1967 -- 2006. [ pdf],
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Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, Ruslan R. Salakhutdinov
arXiv [ pdf],
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Exploiting Compositionality to Explore a Large Space of Model Structures
Roger Grosse, Ruslan Salakhutdinov, William Freeman, and Joshua Tenenbaum
UAI 2012 [ pdf].
Best student paper award (Congratulations Roger). -
One-Shot Learning with a Hierarchical Nonparametric Bayesian Model
Ruslan Salakhutdinov, Josh Tenenbaum, and Antonio Torralba
JMLR WC&P Unsupervised and Transfer Learning, 2012, [ pdf] ` -
Deep Lambertian Networks
Yichuan Tang , Ruslan Salakhut dinov, and Geoffrey Hinton
The 29th International Conference on Machine Learning (ICML 2012) [ pdf],
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Deep Mixtures of Factor Analysers
Yichuan Tang , Ruslan Salakhut dinov, and Geoffrey Hinton
The 29th International Conference on Machine Learning (ICML 2012) [ pdf],
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Concept learning as motor program induction: A large-scale empirical study.
Brenden Lake , Ruslan Salakhutdinov, and Josh Tenenbaum.
Proceedings of the 34rd Annual Conference of the Cognitive Science Society, 2012 [ pdf], Supporting Info
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Robust Boltzmann Machines for Recognition and Denoising
Yichuan Tang , Ruslan Salakhut dinov, and Geoffrey Hinton
IEEE Computer Vision and Pattern Recognition (CVPR) 2012. [ pdf]
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Resource Configurable Spoken Query Detection using Deep Boltzmann Machines
Yaodong Zhang, Ruslan Salakhutdinov, Hung-An Chang, and James Glass.
37th International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2012 [ pdf]
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Domain Adaptation: A Small Sample Statistical Approach
Dean Foster, Sham Kakade, and Ruslan Salakhutdinov
JMLR W&CP 15 (AISTATS), 2012 [ pdf]
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Learning to Learn with Compound Hierarchical-Deep Models
Ruslan Salakhutdinov, Josh Tenenbaum , Antonio Torralba
Neural Information Processing Systems (NIPS 25), 2011, [ pdf]
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Transfer Learning by Borrowing Examples
Joseph Lim , Ruslan Salakhutdinov Antonio Torralba
Neural Information Processing Systems (NIPS 25). 2011, [ pdf]
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Learning with the Weighted Trace-norm under Arbitrary Sampling Distributions
Rina Foygel, Ruslan Salakhutdinov, Ohad Shamir, Nathan Srebro
Neural Information Processing Systems (NIPS 25), 2011, [ pdf]
Supplementary material [ pdf] -
One-shot learning of simple visual concepts
Brenden Lake , Ruslan Salakhutdinov, Jason Gross, and Josh Tenenbaum.
Proceedings of the 33rd Annual Conference of the Cognitive Science Society, 2011 [ pdf], videos -
Learning to Share Visual Appearance for Multiclass Object Detection
Ruslan Salakhutdinov, Antonio Torralba , and Josh Tenenbaum.
IEEE Computer Vision and Pattern Recognition (CVPR) 2011 [ pdf] -
Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm.
Ruslan Salakhutdinov and Nathan Srebro.
Neural Information Processing Systems 24, 2011
[bibtex] [ pdf]
Earlier version: [arXiv:1002.2780v1], [ps.gz][ pdf] -
Practical Large-Scale Optimization for Max-Norm Regularization.
Jason Lee, Benjamin Recht, Ruslan Salakhutdinov, Nathan Srebro, and Joel A. Tropp
Neural Information Processing Systems 24, 2011
[bibtex] [ pdf]
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Discovering Binary Codes for Documents by Learning Deep Generative Models.
Geoffrey Hinton and Ruslan Salakhutdinov.
Topics in Cognitive Science, 2010
[bibtex] [ pdf] -
One-Shot Learning with a Hierarchical Nonparametric Bayesian Model.
Ruslan Salakhutdinov, Josh Tenenbaum, and Antonio Torralba.
MIT Technical Report MIT-CSAIL-TR-2010-052, 2010, [ pdf] -
Learning in Deep Boltzmann Machines using Adaptive MCMC.
Ruslan Salakhutdinov.
In 27th International Conference on Machine Learning (ICML-2010)
[bibtex] [ps.gz], [ pdf] -
Efficient Learning of Deep Boltzmann Machines.
Ruslan Salakhutdinov and Hugo Larochelle.
AI and Statistics, 2010
[bibtex] [ps.gz][ pdf] -
Learning in Markov Random Fields using Tempered Transitions.
Ruslan Salakhutdinov.
Neural Information Processing Systems 23, 2010
[bibtex] [ps.gz][ pdf] -
Replicated Softmax: an Undirected Topic Model.
Ruslan Salakhutdinov and Geoffrey Hinton.
Neural Information Processing Systems 23, 2010
[bibtex] [ps.gz][pdf] -
Modelling Relational Data using Bayesian Clustered Tensor Factorization.
Ilya Sutskever, Ruslan Salakhutdinov, and Josh Tenenbaum.
Neural Information Processing Systems 23, 2010
[bibtex] [pdf]
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Learning Deep Generative Models.
Ruslan Salakhutdinov
PhD Thesis, Sep 2009
Dept. of Computer Science, University of Toronto
[bibtex] [ps.gz][pdf] -
Semantic Hashing.
Ruslan Salakhutdinov and Geoffrey Hinton
International Journal of Approximate Reasoning, 2009
[bibtex] [pdf]
Earlier verision appeared in: SIGIR workshop on Information Retrieval and applications of Graphical Models (2007)
[bibtex] [ps.gz, pdf] -
Learning Nonlinear Dynamic Models.
John Langford, Ruslan Salakhutdinov and Tong Zhang.
Proceedings of the 26th International Conference on Machine Learning (ICML), 2009.
[bibtex] [ps.gz][ pdf] -
Evaluation Methods for Topic Models.
Hanna M. Wallach, Iain Murray, Ruslan Salakhutdinov and David Mimno.
Proceedings of the 26th International Conference on Machine Learning (ICML), 2009.
[bibtex] [ pdf] -
Deep Boltzmann Machines
Ruslan Salakhutdinov and Geoffrey Hinton
12th International Conference on Artificial Intelligence and Statistics (2009).
[bibtex] [ps.gz][ pdf] -
Evaluating probabilities under high-dimensional latent variable models.
Iain Murray and Ruslan Salakhutdinov
Neural Information Processing Systems 22 (NIPS 2009)
[bibtex] [ pdf], Jan 2009
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Learning and Evaluating Boltzmann Machines
Ruslan Salakhutdinov
Technical Report UTML TR 2008-002, Dept. of Computer Science, University of Toronto
[bibtex] [ps.gz][ pdf]
This paper introduces a new Boltzmann machine learning algorithm that combines variational techniques and MCMC. -
On the Quantitative Analysis of Deep Belief Networks.
Ruslan Salakhutdinov and Iain Murray
In 25th International Conference on Machine Learning (ICML-2008)
[bibtex] [ps.gz],[ pdf], [code] -
Bayesian Probabilistic Matrix Factorization using MCMC.
Ruslan Salakhutdinov and Andriy Mnih
In 25th International Conference on Machine Learning (ICML-2008)
[bibtex] [ps.gz],[ pdf] -
Probabilistic Matrix Factorization.
Ruslan Salakhutdinov and Andriy Mnih
Neural Information Processing Systems 21 (NIPS 2008)
[bibtex] [ps.gz][pdf], Jan 2008, oral. -
Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes.
Ruslan Salakhutdinov and Geoffrey Hinton
Neural Information Processing Systems 21 (NIPS 2008)
[bibtex] [ps.gz][pdf], Jan 2008 -
Restricted Boltzmann Machines for Collaborative Filtering.
Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey Hinton
ICML 2007
[bibtex] [ps.gz][pdf] -
Learning a Nonlinear Embedding by Preserving Class Neighbourhood Structure.
Ruslan Salakhutdinov and Geoffrey Hinton
AI and Statistics 2007
[bibtex] [ps.gz][ pdf] -
Reducing the Dimensionality of Data with Neural Networks.
Geoffrey E. Hinton and Ruslan R. Salakhutdinov
Science, 28 July 2006:
Vol. 313. no. 5786, pp. 504 - 507
[bibtex] [pdf][ Science Online]
Supporting Online Material [pdf, Science Online]
Matlab Code is available here
Figures are available in eps format: [fig1, fig2, fig3, fig4]
and in jpeg format: [fig1, fig2, fig3, fig4] -
Simultaneous Localization and Surveying with Multiple Agents.
Sam Roweis & Ruslan Salakhutdinov (2005)
In R. Murray-Smith, R. Shorten (eds), Switching and Learning in Feedback Systems (Springer LNCS vol 3355, 2005). pp. 313--332
[bibtex] [pdf] -
Neighbourhood Component Analysis
Jacob Goldberger, Sam Roweis, Geoff Hinton, Ruslan Salakhutdinov
Neural Information Processing Systems 17 (NIPS'04).
[bibtex] [pdf] -
Semi-Supervised Mixture-of-Experts Classification
Grigoris Karakoulas & Ruslan Salakhutdinov
The Fourth IEEE International Conference on Data Mining (ICDM 04)
[bibtex] -
On the Convergence of Bound Optimization Algorithms
Ruslan Salakhutdinov & Sam T. Roweis & Zoubin Ghahramani (2003).
Uncertainty in Artificial Intelligence (UAI-2003). pp 509-516
[bibtex] [ps.gz] [pdf] -
Optimization with EM and Expectation-Conjugate-Gradient
Ruslan Salakhutdinov & Sam T. Roweis & Zoubin Ghahramani (2003).
International Conference on Machine Learning (ICML-2003). pp 672-679
[bibtex] [ps.gz] [pdf] -
Adaptive Overrelaxed Bound Optimization Methods.
Ruslan Salakhutdinov & Sam T. Roweis (2003).
International Conference on Machine Learning (ICML-2003). pp 664-671
[bibtex] [ps.gz] [pdf]Also check out demos on Adaptive vs Standard EM for Mixture of Factor Analyzers here and Mixture of Gaussians here
- Notes on the KL-divergence between a Markov chain and its equilibrium distribution
Iain Murray and Ruslan Salakhutdinov (2008)
[pdf] -
Relationship between gradient and EM steps in latent variable models.
Ruslan Salakhutdinov & Sam T. Roweis & Zoubin Ghahramani (2002).
Unpublished Report. [draft version (sep.02)-->ps.gz(32K) pdf(70K)] -
Expectation Conjugate-Gradient: An Alternative to EM
Ruslan Salakhutdinov & Sam T. Roweis & Zoubin Ghahramani (2003).
[draft version (june.02)-->ps.gz(186K) pdf(640K)]
Technical Reports/Unpublished Manuscripts
- Notes on the KL-divergence between a Markov chain and its equilibrium distribution