Saarland University, Machine Learning Group, Fak. 6 - Mathematik und Informatik, Campus E1 1, 66123 Saarbücken, Germany     

Machine Learning Group
Department of Mathematics and Computer Science - Saarland University


Researcher/Ph.D. Student,
Faculty of Mathematics and Computer Science,
Saarland University

Building E 1 1, Room 229
Universität des Saarlandes
Postfach 15 11 50
D - 66041 Saarbrücken




Since 2015, I'm a Researcher / PhD student in the Machine Learning Group of Prof. Matthias Hein. My general research interests are in optimization, machine learning and computer vision.
I got my M.Sc. degree in Computer Science in 2015 from Saarland University. In my master thesis, I worked on hypergraph matching and tensor optimization. Currently, I am trying to further extend my work, in particular, for solving other interesting problems in machine learning and computer vision.




  • Q. Nguyen and M. Hein
    The loss surface of deep and wide neural networks
    accepted at ICML, preprint available at arXiv
  • A. Gautier, Q. Nguyen and M. Hein
    Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods
    NIPS 2016. PDF  Supplement: PDF  (code will be available soon)
  • Q. Nguyen, A. Gautier and M. Hein
    Nonlinear Spectral Methods for Nonconvex Optimization with Global Optimality
    NIPS'16 Workshop On Optimization for Machine Learning. PDF 
  • Y. Xian, Z. Akata, G. Sharma, Q. Nguyen, M. Hein and B. Schiele
    Latent Embeddings for Zero-shot Classification
    Spotlight at CVPR 2016. arXiv
    (project page)
  • Q. Nguyen, F. Tudisco, A. Gautier and M. Hein
    An Efficient Multilinear Optimization Framework for Hypergraph Matching
    accepted at IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 2016, preprint available at arXiv
  • Q. Nguyen, A. Gautier and M. Hein
    A Flexible Tensor Block Coordinate Ascent Scheme for Hypergraph Matching
    CVPR 2015 (oral presentation). PDF  Supplement: PDF 
    Extended Abstract: PDF  Poster: PDF  Slide: PDF 
    (project page)