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Andriy mnih phd thesis proposal

Andriy mnih phd thesis proposal Belief Systems         Andriy

Andriy Mnih

My email could be produced within the URL using this page.

I’m an analysis investigator at Google DeepMind. Until Feb 2013, I had been a postdoctoral investigator at Gatsby, dealing with Yee Whye Teh. Before that we’ll be considered a PhD student within the Machine Learning Group inside the College of Toronto, advised by Geoffrey Hinton.

Research interests

  • latent variable models
  • variational inference
  • representation learning
  • record language modelling

Publications

    Variational inference for Monte Carlo objectives
    Andriy Mnih and Danilo J. Rezende
    Worldwide Conference on Machine Learning 2016 (ICML 2016) [arxiv] [slides] [poster] [bibtex]

MuProp: Impartial Backpropagation for Stochastic Neural Systems
Shixiang Gu, Sergey Levine, Ilya Sutskever, Andriy Mnih
ICLR 2016 [arxiv]

Neural Variational Inference and Learning in Belief Systems
Andriy Mnih and Karol Gregor
Worldwide Conference on Machine Learning 2014 (ICML 2014) [pdf] [slides] [poster] [bibtex] [talk]

Deep AutoRegressive Systems
Karol Gregor, Ivo Danihelka, Andriy Mnih, Charles Blundell, Daan Wierstra
Worldwide Conference on Machine Learning 2014 (ICML 2014) [pdf] [bibtex]

Learning word embeddings efficiently with noise-contrastive estimation
Andriy Mnih and Koray Kavukcuoglu
Advances in Neural Information Processing Systems 26 (NIPS 2013) [pdf] [poster] [bibtex]

Learning Label Trees for Probabilistic Modelling of Implicit Feedback
Andriy Mnih and Yee Whye Teh
Advances in Neural Information Processing Systems 25 (NIPS 2012) [pdf] [poster] [bibtex]

A quick and easy formula for training neural probabilistic language models
Andriy Mnih and Yee Whye Teh
Worldwide Conference on Machine Learning 2012 (ICML 2012) [pdf] [slides] [poster] [bibtex] [5 min talk]

Andriy mnih phd thesis proposal Restricted Boltzmann Machines for Collaborative

Taxonomy-Informed Latent Factor Models for Implicit Feedback
Andriy Mnih
JMLR W&Clubpenguin Volume 18: Proceedings of KDD Cup 2011 [pdf] [slides] [bibtex]

Learning Distributed Representations for Record Language Modelling and Collaborative Filtering
Andriy Mnih
PhD Thesis, College of Toronto, 2009 [pdf] [bibtex]

Improving accurate documentation Language Model Through Non-straight line Conjecture
Andriy Mnih, Zhang Yuecheng, and Geoffrey Hinton
Neurocomputing, 72:7-9, 2009 [bibtex]

A Scalable Hierarchical Distributed Language Model
Andriy Mnih and Geoffrey Hinton
Advances in Neural Information Processing Systems 21 (NIPS 2008) [pdf] [bibtex]

Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo
Ruslan Salakhutdinov and Andriy Mnih
Worldwide Conference on Machine Learning 2008 (ICML 2008) [pdf] [bibtex]

Improving accurate documentation Language Model by Modulating the final results of Context Words
Zhang Yuecheng, Andriy Mnih, and Geoffrey Hinton
European Symposium on Artificial Neural Systems 2008 (ESANN 2008)

Probabilistic Matrix Factorization
Ruslan Salakhutdinov and Andriy Mnih
Advances in Neural Information Processing Systems 20 (NIPS 2007) [pdf] [bibtex]

Three New Graphical Models for Record Language Modelling
Andriy Mnih and Geoffrey Hinton
Worldwide Conference on Machine Learning 2007 (ICML 2007) [pdf] [bibtex]

Restricted Boltzmann Machines for Collaborative Filtering
Ruslan Salakhutdinov, Andriy Mnih, and Geoffrey Hinton
Worldwide Conference on Machine Learning 2007 (ICML 2007) [pdf] [bibtex]

Andriy mnih phd thesis proposal Three New Graphical Models for

Visualizing Similarity Data with a mixture of Maps
James Prepare, Ilya Sutskever, Andriy Mnih, and Geoffrey Hinton
AI and Statistics 2007 (AISTATS 2007) [pdf] [bibtex]

Learning Nonlinear Constraints with Contrastive Backpropagation
Andriy Mnih and Geoffrey Hinton
Worldwide Joint Conference on Neural Systems 2005 (IJCNN 2005) [bibtex]

Wormholes Improve Contrastive Divergence
Geoffrey Hinton, Max Welling, and Andriy Mnih
Advances in Neural Information Processing Systems 16 (NIPS 2003) [bibtex]


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