Bronstein, Michael M., Joan Bruna, Taco Cohen, and Petar Velickovic. 2021. “Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.” CoRR abs/2104.13478.
Cho, Dongjin, Cheolhee Yoo, Jungho Im, and Dong-Hyun Cha. 2020. “Comparative Assessment of Various Machine Learning-Based Bias Correction Methods for Numerical Weather Prediction Model Forecasts of Extreme Air Temperatures in Urban Areas.” Earth and Space Science 7 (4): e2019EA000740.
Cho, Kyunghyun, Bart van Merrienboer, Çaglar Gülçehre, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. “Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation.” CoRR abs/1406.1078.
Dumoulin, Vincent, and Francesco Visin. 2016. A guide to convolution arithmetic for deep learning.” arXiv e-Prints, March, arXiv:1603.07285.
He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. “Deep Residual Learning for Image Recognition.” CoRR abs/1512.03385.
Hochreiter, Sepp, and Jürgen Schmidhuber. 1997. “Long Short-Term Memory.” Neural Computation 9 (8): 1735–80.
Ioffe, Sergey, and Christian Szegedy. 2015. “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.”
Loshchilov, Ilya, and Frank Hutter. 2016. SGDR: Stochastic Gradient Descent with Restarts.” CoRR abs/1608.03983.
Olah, Chris, Alexander Mordvintsev, and Ludwig Schubert. 2017. “Feature Visualization.” Distill.
Osgood, Brad. 2019. Lectures on the Fourier Transform and Its Applications. American Mathematical Society.
Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. 2015. “U-Net: Convolutional Networks for Biomedical Image Segmentation.” CoRR abs/1505.04597.
Sandler, Mark, Andrew G. Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. 2018. “Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation.” CoRR abs/1801.04381.
Smith, Leslie N. 2015. “No More Pesky Learning Rate Guessing Games.” CoRR abs/1506.01186.
Smith, Leslie N., and Nicholay Topin. 2017. “Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates.” CoRR abs/1708.07120.
Srivastava, Nitish, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. “Dropout: A Simple Way to Prevent Neural Networks from Overfitting.” J. Mach. Learn. Res. 15 (1): 1929–58.
Trefethen, Lloyd N., and David Bau. 1997. Numerical Linear Algebra. SIAM.
Vistnes, Arnt Inge. 2018. Physics of Oscillations and Waves. With Use of Matlab and Python. Springer.
Warden, Pete. 2018. “Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition.” CoRR abs/1804.03209.
Zhang, Hongyi, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz. 2017. mixup: Beyond Empirical Risk Minimization.” arXiv e-Prints, October, arXiv:1710.09412.