PhD candidate @ IDSIA
University of Lugano & SUPSI
Email: rupesh _at_ idsia _dot_ ch
I am a PhD student in artificial intelligence and machine learning, supervised by Prof. Jürgen Schmidhuber at IDSIA. I am currently spending my time understanding artificial neural networks, and their use in the field of Deep Learning.
You may want to have a look at my publications below or my profile on Google Scholar.
12/06/15 We have won the first MS COCO Image Captioning Challenge! We tied for first place with Google. See leaderboard and paper.
11/05/15 Our extended abstract on Highway Networks will be presented at the ICML Deep Learning workshop in Lille. Full paper and Code coming soon!
20/03/15 Our paper on locally competitive networks has been accepted to ICLR 2015 Conference Track!
10/03/15: Our work on caption generation has been accepted to CVPR 2015!
K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink and J. Schmidhuber: LSTM: A Search Space Odyssey. (2015) arXiv:1503.04069
Conferences & Workshops:
R. K. Srivastava, J. Masci, F. Gomez and J. Schmidhuber: Understanding Locally Competitve Networks. Accepted: International Conference on Learning Representations: Conference Track (ICLR 2015). arXiv:1410.1165
H. Fang^, S. Gupta^, F. Iandola^, R. K. Srivastava^, L. Deng, P. Dollár, J. Gao, X. He, M. Mitchell, J. Platt, C. L. Zitnick, G. Zweig: From Captions to Visual Concepts and Back. Accepted: IEEE International Concference on Computer Vision and Patter Recognition (CVPR 2015). arXiv:1411.4952.
R. K. Srivastava, J. Masci, F. Gomez and J. Schmidhuber: Understanding Locally Competitve Networks. NIPS Deep Learning Workshop (2014) arXiv:1410.1165
R. K. Srivastava, J. Masci, S. Kazerounian, F. Gomez, J. Schmidhuber: Compete to Compute. In: Advances in Neural Information Processing Systems (NIPS 2013) pdf
R. K. Srivastava, B. R. Steunebrink, M. Stollenga and J. Schmidhuber: Continually Adding Self-Invented Problems to the Repertoire: First Experiments with PowerPlay. In: IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL) (2012) pdf
R. K. Srivastava, J. Schmidhuber and F. Gomez: Generalized Compressed Network Search. In: Parallel Problem Solving from Nature, pp. 337–346 (PPSN 2012) pdf
R. K. Srivastava, J. Schmidhuber and F. Gomez: Generalized Compressed Network Search. In: Proceedings of the 14th international conference on Genetic and Evolutionary Computation (Late Breaking Abstract), pp. 647–648 (2012) pdf
R. K. Srivastava and K. Deb: An EA-based Approach to Design Optimization using Evidence Theory. In: Proceedings of the 13th annual conference on Genetic and Evolutionary Computation, pp. 1139–1146 (GECCO 2011) pdf
R. K. Srivastava and K. Deb: Bayesian Reliability Analysis under Incomplete Information using Evolutionary Algorithms. In: 8th International Conference on Simulated Evolution And Learning, Lecture Notes in Computer Science, vol. 6457, pp. 435–444, Springer (SEAL 2010) pdf
K. Nishijima, Y. Sun, R. K. Srivastava, H. Ogai and B. Bhattacharya: Advanced pipe inspection robot using rotating probe. In: 15th International Symposium on Artificial Life and Robotics, Beppu, Japan, pp. 573–576 (2010) pdf
R. K. Srivastava, B. R. Steunebrink and J. Schmidhuber: First Experiments with PowerPlay. In: Neural Networks (2013) preprint
R. K. Srivastava, and K. Deb: An Evolutionary Algorithm based Approach to Design Optimization using Evidence Theory. To appear: ASME Journal of Mechanical Design (2013) preprint
R. K. Srivastava, and K. Deb: An Evolutionary based Bayesian Design Optimization Approach under Incomplete Information. Engineering Optimization, Vol. 45, No. 2, pp. 141-165 (2013) preprint
R. K. Srivastava, J. Masci, S. Kazerounian, F. Gomez and J. Schmidhuber: Compete to Compute. IDSIA Tech Report 2013. pdf