References

ABC+16

Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, and others. Tensorflow: a system for large-scale machine learning. In 12th $\$USENIX$\$ Symposium on Operating Systems Design and Implementation ($\$OSDI$\$ 16), 265–283. 2016.

Cho16

Francois Chollet. Building autoencoders in keras. The Keras Blog, 2016.

C+15

Francois Chollet and others. Keras. 2015. URL: https://github.com/fchollet/keras.

DV16

Vincent Dumoulin and Francesco Visin. A guide to convolution arithmetic for deep learning. arXiv preprint arXiv:1603.07285, 2016.

Geron19

Aurélien Géron. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O'Reilly Media, 2019.

MPB15

Ali Mousavi, Ankit B Patel, and Richard G Baraniuk. A deep learning approach to structured signal recovery. In 2015 53rd annual allerton conference on communication, control, and computing (Allerton), 1336–1343. IEEE, 2015.

VdWSchonbergerNI+14

Stefan Van der Walt, Johannes L Schönberger, Juan Nunez-Iglesias, François Boulogne, Joshua D Warner, Neil Yager, Emmanuelle Gouillart, and Tony Yu. Scikit-image: image processing in python. PeerJ, 2:e453, 2014.

WAS08

Zhongmin Wang, Gonzalo R Arce, and Brian M Sadler. Subspace compressive detection for sparse signals. In Acoustics, Speech and Signal Processing, 2008. ICASSP 2008. IEEE International Conference on, 3873–3876. IEEE, 2008.