Selected Materials for Beginners

follow the below sections.

  • Tensor: from Linear Algebra to Multilinear Algebra

  • Tensor implementations

  • Big data analysis

  • Deep learning and deep reinforcement learning

  • Various applications

  • M. Jordan,  T. M. Mitchell. Machine learning: Trends, perspectives, and prospects. Science, 349, no. 6245, pp. 255-260, 2015.

  • Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. Nature,  521.7553,  pp. 436-444, 2015.
  • R.G. Baraniuk. More is less: signal processing and the data deluge. Science, 331.6018, pp. 717-719, 2011.

  • E. Papalexakis, F. Christos, N. D. Sidiropoulos. Tensors for data mining and data fusion: Models, applications, and scalable algorithms. ACM Transactions on Intelligent Systems and Technology (TIST), 8.2, 2017.
  • X.-Y. Liu, X. Wang. LS-decomposition for robust recovery of sensory big data. IEEE Transactions on Big Data, 2017.

Tensor: from Linear Algebra to Multilinear Algebra

The basics for Tensor.

Linear Algebra

Tensor Basics

  • T.G. Kolda, B. W. Bader. Tensor decompositions and applications. SIAM Review, 51.3,  pp. 455-500, 2009.
  • Cichocki, A., Mandic, D., De Lathauwer, L., Zhou, G., Zhao, Q., Caiafa, C. and Phan, H.A. Tensor decompositions for signal processing applications: From two-way to multiway component analysis. IEEE Signal Processing Magazine, 32(2), pp.145-163, 2015.

  • Sidiropoulos, N.D., De Lathauwer, L., Fu, X., Huang, K., Papalexakis, E.E. and Faloutsos, C. Tensor decomposition for signal processing and machine learning. IEEE Transactions on Signal Processing, 65(13), pp.3551-3582, 2017.

Tensor Toolbox

Tensor Implementations

  • N. Vervliet, O. Debals, L. De Lathauwer. November. Tensorlab 3.0—Numerical optimization strategies for large-scale constrained and coupled matrix/tensor factorization. In 50th Asilomar Conference on Signals, Systems and Computers, pp. 1733-1738, 2016.

Tensor Surveys

  • Q. Song, H. Ge, J. Caverlee, X. Hu. Tensor completion algorithms in big data analytics. ACM Transactions on Knowledge Discovery from Data (TKDD), 2019.

Deep Learning and Deep Reinforcement Learning

The basics for deep learning and deep reinforcement learning.

Extended Readings

More reading for your reference.

Alan Turing’s papers

  • Turing, A.M., 2009. Computing machinery and intelligence. In Parsing the Turing Test (pp. 23-65). Springer, Dordrecht.
  • Turing, A.M., 1937. On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London mathematical society, 2(1), pp.230-265.
  • Turing, A.M., 1995. Computing machinery and intelligence. Brian Physiology and Psychology, 213.
  • Turing, A.M., 1954. Solvable and unsolvable problems. Penguin Books.

Recent Developments in AI

  • V. Mnih, K. Kavukcuoglu, D. Silver, A.A. Rusu, J. Veness, M.G.  Bellemare, A. Graves, M. Riedmiller, A.K. Fidjeland, G. Ostrovski, S. Petersen. Human-level control through deep reinforcement learning. Nature, 518(7540), p.529, 2015.

  • D. Silver, A.  Huang, C.J.  Maddison, A. Guez, A, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M.  Lanctot, S. Dieleman. Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), pp. 484, 2016.

  • D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez,  T. Hubert, L. Baker, M. Lai, A. Bolton, Y. Chen. Mastering the game of Go without human knowledge.  Nature, 550(7676), p.354, 2017.

Writing, Revising and Polishing Your Manuscripts

Suggestion for your Manuscripts..

To polish AI/ML/DL manuscripts that target at conferences (NIPS, CVPR, AAAI, ICML, ICLR, IJCAI, ACL, etc.),  please check review comments on Open Review

Interesting Papers

  • Heisenberg’s invention of matrices. Pradeep Kumar, 2017.

Videos for Animated math 

For manuscripts