Papers: Generalization
In Progress
- Zhang, C., Bengio, S., Hardt, M., Recht, B. and Vinyals, O., 2016. Understanding deep learning requires rethinking generalization. arXiv preprint arXiv:1611.03530. notes
- Kawaguchi, K., Kaelbling, L.P. and Bengio, Y., 2017. Generalization in deep learning. arXiv preprint arXiv:1710.05468. notes
- Choromanska, A., Henaff, M., Mathieu, M., Arous, G.B. and LeCun, Y., 2015, February. The loss surfaces of multilayer networks. In Artificial Intelligence and Statistics (pp. 192-204). notes
- Baldi, P. and Hornik, K., 1989. Neural networks and principal component analysis: Learning from examples without local minima. Neural networks, 2(1), pp.53-58. notes
- Kawaguchi, K., 2016. Deep learning without poor local minima. In Advances in Neural Information Processing Systems (pp. 586-594). notes
To Do
- Han, S., Mao, H. and Dally, W.J., 2015. Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149.
Completed
- Denil, M., Shakibi, B., Dinh, L. and De Freitas, N., 2013. Predicting parameters in deep learning. In Advances in neural information processing systems (pp. 2148-2156).notes
- Denton, E.L., Zaremba, W., Bruna, J., LeCun, Y. and Fergus, R., 2014. Exploiting linear structure within convolutional networks for efficient evaluation. In Advances in neural information processing systems (pp. 1269-1277). notes
Papers: AutoML
In Progress
- Dikov, G., van der Smagt, P. and Bayer, J., 2019. Bayesian Learning of Neural Network Architectures. arXiv preprint arXiv:1901.04436.
- Liu, H., Simonyan, K. and Yang, Y., 2018. Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055.
- Pham, H., Guan, M.Y., Zoph, B., Le, Q.V. and Dean, J., 2018. Efficient neural architecture search via parameter sharing. arXiv preprint arXiv:1802.03268.
To Do
Completed
- Liu, C., Zoph, B., Neumann, M., Shlens, J., Hua, W., Li, L.J., Fei-Fei, L., Yuille, A., Huang, J. and Murphy, K., 2018. Progressive neural architecture search. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 19-34). notes
Papers: Object Detection
In Progress
To Do
Completed
- Redmon, J., Divvala, S., Girshick, R. and Farhadi, A., 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
- Redmon, J. and Farhadi, A., 2017. YOLO9000: better, faster, stronger. arXiv preprint.
- Redmon, J. and Farhadi, A., 2018. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.
Books
In Progress
- Ziemer, W.P. and Torres, M., 2017. Modern real analysis (Vol. 278). Springer.
- Luenberger, D.G. and Ye, Y., 1984. Linear and nonlinear programming (Vol. 2). Reading, MA: Addison-wesley.
To Do
- Kevin P. Murphy. 2012. Machine Learning: A Probabilistic Perspective. The MIT Press.
Completed
- Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag, Berlin, Heidelberg.
