AI Reading List: Technologies: Neural Networks
Below is a portion of my informal list of readings related to Artificial Intelligence (AI). This started out as a very short list created for use in conjunction with an academic presentation and has now grown much larger. Please let me know if you have any corrections, additions, suggestions, etc. It is very idiosyncratic and not meant to be comprehensive. Please feel free to share with others.
Artificial Intelligence (AI) Reading List, by Philip Rubin
Technologies — Neural Networks:
Prakash. Specifics of Deep Neural Networks and Bottlenecks (1c). Medium, Aug. 3, 2025.
Prakash. Neural Networks Mechanics (1b). Medium, Aug. 3, 2025.
Prakash. Understanding Deep Neural Networks: Foundations and Intuition (1a). Medium, Aug. 2, 2025.
Xu Tan, Tao Qin, Frank Soong, Tie-Yan Liu. A Survey on Neural Speech Synthesis. arXiv:2106.15561, 2021.
Tomaso Poggio, Andrzej Banburski, and Qianli Liao. Theoretical issues in deep networks. PNAS, June 9, 2020.
James L. McClelland, Felix Hill, Maja Rudolph, Jason Baldridge, and Hinrich Schütze. Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models. Proceedings of the National Academy of Sciences, 117(42), 25966-25974, 2020. DOI: 10.1073/pnas.1910416117.
Victor Zhou. Machine Learning for Beginners: An Introduction to Neural Networks. A simple explanation of how they work and how to implement one from scratch in Python. March 3, 2019.
Larry Hardesty. Explained: Neural Networks. MIT News, April 14, 2017.
Tomaso Poggio, Hrushikesh Mhaskar, Lorenzo Rosasco, Brando Miranda, and Qianli Liao. Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. International Journal of Automation and Computing, March 14, 2017.
Dan Roth, University of Pennsylvania. Neural Networks Overview 1 Introduction (PDF). Oct. 4, 2016.
G. Hinton, et al. Deep Neural Networks for Acoustic Modeling in Speech Recognition. IEEE Signal Processing Magazine, vol. 29, no. 6, 82-97, Nov. 2012. (See PDF.)
Vikramjit Mitra, Hosung Nam, Carol Y. Espy-Wilson, Elliot Saltzman, and Louis Goldstein. Retrieving Tract Variables From Acoustics: A Comparison of Different Machine Learning Strategies. IEEE Journal of Selected Topics in Signal Processing, Vol. 4, #6, 1027-1045, Dec. 2010.
Takaaki Kuratate, Kevin G. Munhall, Philip E. Rubin, Eric Vatikiotis-Bateson, and Hania Yehia. Audio-Visual Synthesis of Talking Faces From Speech Production Correlates. Sixth European Conference on Speech Communication and Technology, EuroSpeech 1999, Paper K013, Budapest, Hungary, September 5-9, 1999.
John Hogden, Elliot Saltzman, and Philip Rubin. Unsupervised neural networks that use a continuity constraint to track articulators. The Journal of the Acoustical Society of America, 92, 2477, 1992.
Terrence J. Sejnowski and Charles R. Rosenberg. NETtalk: A parallel network that learns to read aloud. JHU/EECS-86/01: The Johns Hopkins University, Electrical Engineering and Computer Science Department, 1986.
D. E. Rumelhart and J. L. McClelland. On Learning the Past Tenses of English Verbs. Chapter 18 in David E. Rumelhart, James L. McClelland and PDP Research Group. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, 216-268, 1987.
David E. Rumelhart, James L. McClelland and the PDP Research Group. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, The MIT Press: 1987.
J. J. Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences. 79 (8): 1982, 2554–2558. Bibcode:1982PNAS...79.2554H. doi:10.1073/pnas.79.8.2554. PMC 346238. PMID 6953413.
Marvin Minsky and Seymour Papert. Perceptrons: An Introduction to Computational Geometry. MIT Press: 1969 / 2017.
Frank Rosenblatt. The Perceptron: A Probabilistic Model For Information Storage And Organization In The Brain. Psychological Review. 65 (6): 386–408, 1958.
D. Kriesel. A Brief Introduction to Neural Networks.
Amazon AWS. What is a neural network?
IBM. What is a neural network?
Wikipedia. Deep learning-based synthesis.
Wikipedia. History of artificial neural networks.
Wikipedia. Neural network (machine learning).