Deep learning vector quantization

ESANN 2016 - 24th European Symposium on Artificial Neural Networks

Volumn , Issue , 2016, Pages 503-508

  De Vries, Harm ^a   Memisevic, Roland ^a   Courville, Aaron ^a  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; IMAGE RECOGNITION; LEARNING SYSTEMS; NEURAL NETWORKS;

DEEP LEARNING; DEEP NEURAL NETS; HIGH CONFIDENCE; LEARNING VECTOR QUANTIZATION;

VECTOR QUANTIZATION;

EID: 84994149407     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. Yoshua Bengio, Aaron C. Courville, and Pascal Vincent. Unsupervised feature learning and deep learning: A review and new perspectives. CoRR, abs/1206.5538, 2012.
2. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097-1105, 2012.
3. Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. CoRR, abs/1502.03167, 2015.
4. Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus. Intriguing properties of neural networks. CoRR, abs/1312.6199, 2013.
5. Anh Mai Nguyen, Jason Yosinski, and Jeff Clune. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. CoRR, abs/1412.1897, 2014.
6. Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. CoRR, abs/1412.6572, 2014.
7. Atsushi Sato and Keiji Yamada. Generalized learning vector quantization. Advances in neural information processing systems, pages 423-429, 1996.
8. Barbara Hammer, Marc Strickert, and Thomas Villmann. Supervised neural gas with general similarity measure. Neural Processing Letters, 21(1):21-44, 2005.
9. Petra Schneider, Michael Biehl, and Barbara Hammer. Adaptive relevance matrices in learning vector quantization. Neural Computation, 21(12):3532-3561, 2009.
10. Kerstin Bunte, Petra Schneider, Barbara Hammer, Frank-Michael Schleif, Thomas Villmann, and Michael Biehl. Limited rank matrix learning, discriminative dimension reduction and visualization. Neural Networks, 26:159-173, 2012.
11. Harm de Vries. Stationarity and uniqueness of generalized matrix learning vector quantization. MIWOCI Workshop-2013, page 16, 2013.