Hussein Mehana, Director of Engineering, Facebook:
- https://arxiv.org/abs/1502.01710
Patrick Koch, Principal Data Scientist, and Funda Gunes, Sr. Research Statistician Developer, SAS Institute Inc:
1. Bottou, L., Curtis, F. E., Nocedal, J., Optimization Methods for Large-Scale Machine Learning,arXiv:1606.04838 [stat.ML], 2016.
2. Sutskever, I., Martens, J., Dahl, G. and Hinton, G., E. On the importance of initialization and momentum in deep learning, In Proceedings of the 30th international conference on machine learning (ICML-13), Atlanta, GA, pp. 1139–1147, June 2013.
3. Bergstra, J. and Bengio, Y., Random Search for Hyper-Parameter Optimization, J. Machine Learning Research, 13: 281–305, 2012.
4. Sparks, E. R. , Talwalkar, A., Haas, D. , Franklin, M. J., Jordan, M. I., and Kraska, T., Automating Model Search for Large Scale Machine Learning, Proceedings of the Sixth ACM Symposium on Cloud Computing, August 27-29, 2015, Kohala Coast, Hawaii.
5. Local Search Optimization, SAS/OR®
6. SAS® Viya™ Distributed Analytics Platform
Dr. Le Song, Assistant Professor, College of Computing, Georgia Institute of Technology:
- H. Dai, Y. Wang, R. Trivedi and L. Song. Recurrent Coevolutionary Feature Embedding Processes for Recommendation, Recsys Workshop on Deep Learning for Recommendation Systems, 2016. PDF (BEST PAPER) (http://arxiv.org/pdf/1609.
03675.pdf) - H. Dai, B. Dai and L. Song. Discriminative Embeddings of Latent Variable Models for Structured Data, International Conference on Machine Learning (ICML), 2016. PDF (https://arxiv.org/pdf/1603.
05629.pdf) - Dai, B., Xie, B., He, N., Liang, Y., Raj, A., Balcan, M., and Song, L. Scalable Kernel Methods via Doubly Stochastic Gradients. Neural Information Processing Systems (NIPS 2014). PDF (https://arxiv.org/pdf/1407.
5599.pdf)
