Computer Analysis and Modeling of Biological Signals and Systems
Neural Nets 1: Readings and Links
McCulloch and Pitts' neural logical calculus
Warren McCulloch and Walter Pitts, "A logical calculus of the ideas immanent in nervous activity",Bulletin of Mathematical Biophysics, 1943.
F. Rosenblatt, "The Perceptron: A probabilistic model for information storage and organization", Psych. Review 1958
Minsky and Papert on the Perceptron
Marvin Minsky and Seymour Papert, Perceptrons: An introduction to computational geometry (originally published 1969)
Classifying multivariate data: Discriminant analysis vs. the Perceptron
J.J. Hopfield, "Neural networks and physical systems with emergent collective computational abilities", PNAS 1982
David Rumelhart and James McClelland, Parallel Distributed Processing: Explorations in the Microstructure of Cognition (1986)
Andrey Karpathy, "The Unreasonable Efectiveness of Recurrent Neural Networks"
Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, 2016.
Eve Armstrong, "A Neural Networks Approach to Predicting How Things Might Have Turned Out Had I Mustered the Nerve to Ask Barry Cottonfield to the Junior Prom Back in 1997", 4/1/2017.
Joel Hestness et al., "Deep learning scaling is predictable, empirically", 12/1/2017.
Matthew Peters et al., "Deep contextualized word representations", 3/22/2018 [ELMo].
Alec Radford,"Improving Language Understanding with Unsupervised Learning, 6/11/2018" [GPT].
Jacob Devlin et al., "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding", 11/10/2018.
Noah Smith, "Contextual Word Representations: A Contextual Introduction", 2/2019.
Ashley Pilipsiszyn, "Better Language Models and Their Implications", 2/14/2019 [GPT-2].