I am a PhD student studying artificial intelligence and machine learning, supervised by Prof. Jürgen Schmidhuber at IDSIA. I am currently spending my time understanding and improving artificial neural networks.
In the past, I worked on robust evolutionary design optimization with Prof. Kalyanmoy Deb.
You may want to have a look at my publications below or my profile on Google Scholar.
Most recently I have been working on adaptive computation and very deep learning with Highway Networks.
Recurrent Highway Networks J. G. Zilly, R. K. Srivastava, J. Koutnik and J. Schmidhuber arXiv Preprint arXiv:1607.03474
Training Very Deep Networks R. K. Srivastava, K. Greff and J. Schmidhuber Neural Information Processing Systems (NIPS 2015 Spotlight) arXiv:1507.06228
Highway Networks R. K. Srivastava, K. Greff and J. Schmidhuber Deep Learning Workshop (ICML 2015). arXiv:1505.00387 poster
Understanding Locally Competitve Networks R. K. Srivastava, J. Masci, F. Gomez and J. Schmidhuber International Conference on Learning Representations (ICLR 2015 Conference Track) arXiv:1410.1165 Previously presented at the NIPS 2014 Deep Learning Workshop.
From Captions to Visual Concepts and Back 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 IEEE International Conference on Computer Vision and Patter Recognition (CVPR 2015). arXiv:1411.4952
Compete to Compute R. K. Srivastava, J. Masci, S. Kazerounian, F. Gomez and J. Schmidhuber Neural Information Processing Systems (NIPS 2013) pdf
Continually Adding Self-Invented Problems to the Repertoire: First Experiments with PowerPlay R. K. Srivastava, B. R. Steunebrink, M. Stollenga and J. Schmidhuber IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL 2012) pdf
Generalized Compressed Network Search R. K. Srivastava, J. Schmidhuber and F. Gomez Parallel Problem Solving from Nature (PPSN 2012) pdf Previously presented as a GECCO 2012 Late Breaking Abstract.
An EA-based Approach to Design Optimization using Evidence Theory R. K. Srivastava and K. Deb Conference on Genetic and Evolutionary Computation (GECCO 2011) pdf
Bayesian Reliability Analysis under Incomplete Information using Evolutionary Algorithms R. K. Srivastava and K. Deb Simulated Evolution And Learning (SEAL 2010) pdf
Advanced pipe inspection robot using rotating probe K. Nishijima, Y. Sun, R. K. Srivastava, H. Ogai and B. Bhattacharya International Symposium on Artificial Life and Robotics (AROB 2010) pdf
First Experiments with PowerPlay R. K. Srivastava, B. R. Steunebrink and J. Schmidhuber Neural Networks (2013) preprint
An Evolutionary Algorithm based Approach to Design Optimization using Evidence Theory R. K. Srivastava, K. Deb and R. Tulshyan ASME Journal of Mechanical Design (2013) preprint
An Evolutionary based Bayesian Design Optimization Approach under Incomplete Information R. K. Srivastava and K. Deb Engineering Optimization (2013) preprint
LSTM: A Search Space Odyssey K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink and J. Schmidhuber IEEE Transactions on Neural Networks and Learning Systems arXiv:1503.04069
Compete to Compute R. K. Srivastava, J. Masci, S. Kazerounian, F. Gomez and J. Schmidhuber IDSIA Tech Report 2013 pdf
Last Modified: 20/09/2015