Structure optimization for deep multimodal fusion networks using graph-induced kernels

ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Volumn , Issue , 2017, Pages 11-16

  Ramachandram, Dhanesh ^a   Lisicki, Michal ^a   Shields, Timothy J ^b   Amer, Mohamed R ^b   Taylor, Graham W ^a  

^a UNIVERSITY OF GUELPH   (Canada)

^b SRI INTERNATIONAL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARCHITECTURE; HUMAN ENGINEERING; MACHINE LEARNING; NETWORK ARCHITECTURE; NEURAL NETWORKS; STRUCTURAL OPTIMIZATION; TREES (MATHEMATICS);

DISCRETE OPTIMIZATION PROBLEMS; HUMAN ACTIVITY RECOGNITION; LEARNING ARCHITECTURES; MULTIMODAL ARCHITECTURES; MULTIMODAL RECOGNITION; NONLINEAR FEATURE EXTRACTION; STRUCTURAL SIMILARITY; STRUCTURE OPTIMIZATION;

DEEP LEARNING;

EID: 85040311350     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1-9, 2015.
2. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016.
3. James Bergstra and Yoshua Bengio. Random search for hyper-parameter opti-mization. Journal of Machine Learning Research, 13:281-305, February 2012.
4. Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P Adams, and Nando de Fre-itas. Taking the human out of the loop: A review of Bayesian optimization. Proc. IEEE, 104(1):148-175, 2016.
5. Jaeyong Sung, Colin Ponce, Bart Selman, and Ashutosh Saxena. Human activity detection from rgbd images. In In AAAI workshop on Pattern, Activity and Intent Recognition (PAIR, 2011.
6. S. Escalera, X. Baro, J. Gonzalez, M.A. Bautista, M. Madadi, M. Reyes, V. Ponce, H.J. Escalante, J. Shotton, and I. Guyon. Chalearn looking at people challenge 2014: Dataset and results. In ECCV-W, 2014.
7. N. Neverova, C. Wolf, G. Taylor, and F. Nebout. Moddrop: Adaptive multi-modal gesture recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, PP(99):1-1, 2015.
8. GPy. GPy: A gaussian process framework in python. http://github.com/ SheffieldML/GPy, since 2012.
9. Lasagne. Lasagne: Lightweight library to build and train neural networks in Theano. https://github.com/Lasagne/Lasagne, since 2015.
10. James S Bergstra, Remi Bardenet, Yoshua Bengio, and Balazs Kegl. Algorithms for Hyper-Parameter optimization. In J Shawe-Taylor, R S Zemel, P L Bartlett, F Pereira, and K Q Weinberger, editors, Advances in Neural Information Process-ing Systems 24, pages 2546-2554. Curran Associates, Inc., 2011.
11. Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown. Sequential model-based optimization for general algorithm configuration. In Learning and Intelligent Op-timization, pages 507-523. Springer, 2011.