In recent years, deep learning has made great progress. It is widely used in many fields, such as computer vision, speech recognition, natural language processing, bioinformatics, etc., and also has a significant impact on basic science fields, such as applied and computational mathematics, statistical physics, computational chemistry and materials science, life science, etc. However, it should be noted that deep learning as a black-box model, its ability is explored through a large number of experiments. The theory of deep learning has gradually attracted the attention of many researchers, and has made progress in many aspects.

This course is closely around the latest development of deep learning theory. It intends to teach mathematical models, theories, algorithms and numerical experiments related to a series of basic problems from multiple perspectives of deep learning theory. This course is designed for doctoral, postgraduate and senior undergraduate students in all majors, who have basic knowledge of machine learning and deep learning.

The topics and the corresponding material are as follows:

1. Introduction to Deep Learning material slides
2. Algorithmic Regularization material slides
3. Inductive Biases due to Dropout material slides
4. Tractable Landscapes for Nonconvex Optimization material slides
5. Multilayer Convolutional Sparse Coding material slides
6. Vulnerability of Deep Neural Networks material slides
7. Information Bottleneck Theory material slides
8. Neural Tangent Kernel material slides
9. Dynamic System and Deep Learning material slides
10. Dynamic View of Deep Learning material slides
11. Generative Model material slides1 slides2

Mathematical Analysis, Linear Algebra, Mathematical Statistics, Numerical Optimization, Matrix Theory, Fundamentals of Machine Learning and Deep Learning

• Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.
• Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural networks, 2(5), 359-366.
• Ruder, S. (2016). An overview of gradient descent optimization algorithms. arXiv preprint arXiv:1609.04747.
• Kramer, M. A. (1991). Nonlinear principal component analysis using autoassociative neural networks. AIChE journal, 37(2), 233-243.
• Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., ... & Shi, W. (2017). Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4681-4690).
• Lawrence, S., Giles, C. L., Tsoi, A. C., & Back, A. D. (1997). Face recognition: A convolutional neural-network approach. IEEE transactions on neural networks, 8(1), 98-113.
• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
• Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998-6008).

• Arora, S., Cohen, N., Hu, W., & Luo, Y. (2019). Implicit regularization in deep matrix factorization. Advances in Neural Information Processing Systems, 32, 7413-7424.
• Byrd, J., & Lipton, Z. (2019, May). What is the effect of importance weighting in deep learning?. In International Conference on Machine Learning (pp. 872-881). PMLR.
• Gunasekar, S., Lee, J., Soudry, D., & Srebro, N. (2018, July). Characterizing implicit bias in terms of optimization geometry. In International Conference on Machine Learning (pp. 1832-1841). PMLR.
• Gunasekar, S., Woodworth, B., Bhojanapalli, S., Neyshabur, B., & Srebro, N. (2018, February). Implicit regularization in matrix factorization. In 2018 Information Theory and Applications Workshop (ITA) (pp. 1-10). IEEE.
• Soudry, D., Hoffer, E., Nacson, M. S., Gunasekar, S., & Srebro, N. (2018). The implicit bias of gradient descent on separable data. The Journal of Machine Learning Research, 19(1), 2822-2878.
• Xu, D., Ye, Y., & Ruan, C. (2021). Understanding the role of importance weighting for deep learning. arXiv preprint arXiv:2103.15209.

• Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1), 1929-1958.
• Arora, R., Bartlett, P., Mianjy, P., & Srebro, N. (2021, July). Dropout: Explicit forms and capacity control. In International Conference on Machine Learning (pp. 351-361). PMLR.
• Mianjy, P., Arora, R., & Vidal, R. (2018, July). On the implicit bias of dropout. In International Conference on Machine Learning (pp. 3540-3548). PMLR.
• Baldi, P., & Sadowski, P. J. (2013). Understanding dropout. Advances in neural information processing systems, 26, 2814-2822.
• Wager, S., Wang, S., & Liang, P. S. (2013). Dropout training as adaptive regularization. Advances in neural information processing systems, 26, 351-359.
• Cavazza, J., Morerio, P., Haeffele, B., Lane, C., Murino, V., & Vidal, R. (2018, March). Dropout as a low-rank regularizer for matrix factorization. In International Conference on Artificial Intelligence and Statistics (pp. 435-444). PMLR.

• Du, S. S., Zhai, X., Poczos, B., & Singh, A. (2018, September). Gradient Descent Provably Optimizes Over-parameterized Neural Networks. In International Conference on Learning Representations.
• Ge, R., Huang, F., Jin, C., & Yuan, Y. (2015, June). Escaping from saddle points—online stochastic gradient for tensor decomposition. In Conference on learning theory (pp. 797-842). PMLR.
• Ge, R., Lee, J. D., & Ma, T. (2016, December). Matrix completion has no spurious local minimum. In Proceedings of the 30th International Conference on Neural Information Processing Systems (pp. 2981-2989).
• Ge, R., Lee, J. D., & Ma, T. (2017). Learning one-hidden-layer neural networks with landscape design. arXiv preprint arXiv:1711.00501.
• Hardt, M., Ma, T., & Recht, B. (2018). Gradient Descent Learns Linear Dynamical Systems. Journal of Machine Learning Research, 19, 1-44.
• He, F., Wang, B., & Tao, D. (2019, September). Piecewise linear activations substantially shape the loss surfaces of neural networks. In International Conference on Learning Representations.

• Zhang, Z., & Zhang, S. (2021). Towards understanding residual and dilated dense neural networks via convolutional sparse coding. National Science Review, 8(3), nwaa159.
• Papyan, V., Romano, Y., & Elad, M. (2017). Convolutional neural networks analyzed via convolutional sparse coding. The Journal of Machine Learning Research, 18(1), 2887-2938.
• Papyan, V., Sulam, J., & Elad, M. (2017). Working locally thinking globally: Theoretical guarantees for convolutional sparse coding. IEEE Transactions on Signal Processing, 65(21), 5687-5701.
• Olshausen, B. A., & Field, D. J. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381(6583), 607-609.
• Zeiler, M., Krishnan, D., Taylor, G., & Fergus, R. (2011). Deconvolutional Networks for Feature Learning. In Comput. Vis. Pattern Recognit.(CVPR), 2010 IEEE Conf (pp. 2528-2535).
• He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
• Pelt, D. M., & Sethian, J. A. (2018). A mixed-scale dense convolutional neural network for image analysis. Proceedings of the National Academy of Sciences, 115(2), 254-259.

• Fawzi, A., Fawzi, H., & Fawzi, O. (2018). Adversarial vulnerability for any classifier. arXiv preprint arXiv:1802.08686.
• Shafahi, A., Huang, W. R., Studer, C., Feizi, S., & Goldstein, T. (2018). Are adversarial examples inevitable?. arXiv preprint arXiv:1809.02104.
• Li, J., Ji, R., Liu, H., Liu, J., Zhong, B., Deng, C., & Tian, Q. (2020). Projection & probability-driven black-box attack. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 362-371).
• Li, Y., Li, L., Wang, L., Zhang, T., & Gong, B. (2019, May). Nattack: Learning the distributions of adversarial examples for an improved black-box attack on deep neural networks. In International Conference on Machine Learning (pp. 3866-3876). PMLR.
• Wu, A., Han, Y., Zhang, Q., & Kuang, X. (2019, July). Untargeted adversarial attack via expanding the semantic gap. In 2019 IEEE International Conference on Multimedia and Expo (ICME) (pp. 514-519). IEEE.
• Rathore, P., Basak, A., Nistala, S. H., & Runkana, V. (2020, July). Untargeted, Targeted and Universal Adversarial Attacks and Defenses on Time Series. In 2020 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.

• Shwartz-Ziv, R., & Tishby, N. (2017). Opening the black box of deep neural networks via information. arXiv preprint arXiv:1703.00810.
• Tishby, N., Pereira, F. C., & Bialek, W. (2000). The information bottleneck method. arXiv preprint physics/0004057.
• Tishby, N., & Zaslavsky, N. (2015, April). Deep learning and the information bottleneck principle. In 2015 IEEE Information Theory Workshop (ITW) (pp. 1-5). IEEE.
• Saxe, A. M., Bansal, Y., Dapello, J., Advani, M., Kolchinsky, A., Tracey, B. D., & Cox, D. D. (2019). On the information bottleneck theory of deep learning. Journal of Statistical Mechanics: Theory and Experiment, 2019(12), 124020.
• Kolchinsky, A., Tracey, B. D., & Wolpert, D. H. (2019). Nonlinear information bottleneck. Entropy, 21(12), 1181.
• Achille, A., & Soatto, S. (2018). Information dropout: Learning optimal representations through noisy computation. IEEE transactions on pattern analysis and machine intelligence, 40(12), 2897-2905.
• Alemi, A. A., Fischer, I., Dillon, J. V., & Murphy, K. (2016). Deep variational information bottleneck. arXiv preprint arXiv:1612.00410.

• Jacot, A., Gabriel, F., & Hongler, C. (2018). Neural tangent kernel: Convergence and generalization in neural networks. arXiv preprint arXiv:1806.07572.
• Lee, J., Xiao, L., Schoenholz, S., Bahri, Y., Novak, R., Sohl-Dickstein, J., & Pennington, J. (2019). Wide neural networks of any depth evolve as linear models under gradient descent. Advances in neural information processing systems, 32, 8572-8583.
• Arora, S., Du, S., Hu, W., Li, Z., & Wang, R. (2019, May). Fine-grained analysis of optimization and generalization for overparameterized two-layer neural networks. In International Conference on Machine Learning (pp. 322-332). PMLR.
• Arora, S., Du, S. S., Hu, W., Li, Z., Salakhutdinov, R., & Wang, R. (2019). On exact computation with an infinitely wide neural net. arXiv preprint arXiv:1904.11955.
• Hu, W., Li, Z., & Yu, D. (2019). Simple and effective regularization methods for training on noisily labeled data with generalization guarantee. arXiv preprint arXiv:1905.11368.

• Weinan, E. (2017). A proposal on machine learning via dynamical systems. Communications in Mathematics and Statistics, 5(1), 1-11.
• Li, Q., Chen, L., & Tai, C. (2017). Maximum principle based algorithms for deep learning. arXiv preprint arXiv:1710.09513.
• Parpas, P., & Muir, C. (2019). Predict globally, correct locally: Parallel-in-time optimal control of neural networks. arXiv preprint arXiv:1902.02542.
• Haber, E., & Ruthotto, L. (2017). Stable architectures for deep neural networks. Inverse problems, 34(1), 014004.
• Lu, Y., Zhong, A., Li, Q., & Dong, B. (2018, July). Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations. In International Conference on Machine Learning (pp. 3276-3285). PMLR.
• Li, Z., & Shi, Z. (2017). Deep residual learning and pdes on manifold. arXiv preprint arXiv:1708.05115.

• Chen, R. T., Rubanova, Y., Bettencourt, J., & Duvenaud, D. (2018). Neural ordinary differential equations. arXiv preprint arXiv:1806.07366.
• Yan, H., Du, J., Tan, V. Y., & Feng, J. (2019). On robustness of neural ordinary differential equations. arXiv preprint arXiv:1910.05513.
• Gai, K., & Zhang, S. (2021). A Mathematical Principle of Deep Learning: Learn the Geodesic Curve in the Wasserstein Space. arXiv preprint arXiv:2102.09235.
• Thorpe, M., & van Gennip, Y. (2018). Deep limits of residual neural networks. arXiv preprint arXiv:1810.11741.
• Lu, Y., Ma, C., Lu, Y., Lu, J., & Ying, L. (2020, November). A mean field analysis of deep ResNet and beyond: Towards provably optimization via overparameterization from depth. In International Conference on Machine Learning (pp. 6426-6436). PMLR.

• Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.
• Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27.
• Arjovsky, M., & Bottou, L. (2017). Towards principled methods for training generative adversarial networks. arXiv preprint arXiv:1701.04862.
• An, D., Guo, Y., Zhang, M., Qi, X., Lei, N., & Gu, X. (2020, August). AE-OT-GAN: Training GANs from data specific latent distribution. In European Conference on Computer Vision (pp. 548-564). Springer, Cham.
• Arjovsky, M., Chintala, S., & Bottou, L. (2017, July). Wasserstein generative adversarial networks. In International conference on machine learning (pp. 214-223). PMLR.
• Tolstikhin, I., Bousquet, O., Gelly, S., & Schoelkopf, B. (2017). Wasserstein auto-encoders. arXiv preprint arXiv:1711.01558.
• Lei, N., An, D., Guo, Y., Su, K., Liu, S., Luo, Z., ... & Gu, X. (2020). A geometric understanding of deep learning. Engineering, 6(3), 361-374.