Pioneering research:
Note: part of the bibliography in this subsection is adapted from CS/ECE 861 course webpage from University of Wisconsin, which is created by Jerry Zhu.
The following framework claims to relate to homotopy type theory?
On a definition of artificial general intelligence
S. Legg, M. Hutter, Universal intelligence: a definition of machine intelligence, Minds & Machines 17, 391–444 (2007) doi
M. Hutter, Universal artificial intelligence: sequential decisions based on algorithmic probability, Springer 2005; book presentation pdf
Shane Legg, Machine super intelligence, PhD thesis, 2008 pdf
Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, Chunfang Liu, A survey on deep transfer learning, arxiv/1808.01974 (a way to address insufficient data in some cases)
Joachim Schmidthuber, Deep learning in neural networks:an overview arxiv/1404.7828; cf. also short html version with some add. links and the beautiful historical insights in his short article Who invented backpropagation? (a g+ version here) and in his scholarpedia Deep learning contribution
David E. Rumelhart, Geoffrey E. Hinton & Ronald J. Williams, Learning representations by back-propagating errors, Nature 323, 533–536 (1986) pdf doi
Hinton, Srivastava, Swersky, Coursera course, Neural networks for machine learning videos
William Gilpin, Cellular automata as convolutional neural networks, arxiv/1809.02942
Eunji Jeong, Joo Seong Jeong, Soojeong Kim, Gyeong-In Yu, Byung-Gon Chun, Improving the expressiveness of deep learning frameworks with recursion, arxiv/1809.00832
Dorjan Hitaj, Luigi V. Mancini, Have you stolen my model? Evasion attacks against deep neural network watermarking techniques, arxiv/1809.00615
Nicholas Polson, Vadim Sokolov, Deep learning: computational aspects, arxiv /1808.08618
Alessandro Betti, Marco Gori, Giuseppe Marra, Backpropagation and biological plausibility, arxiv/1808.06934
Gamaleldin F. Elsayed, Ian Goodfellow, Jascha Sohl-Dickstein, Adversarial reprogramming of neural networks, arxiv/1806.11146; Tom B. Brown etc. and Ian Goodfellow, Unrestricted adversarial examples arxiv/1809.08352
Sanjoy Dasgupta, Charles F. Stevens, Saket Navlakha, A neural algorithm for a fundamental computing problem (motivated by fruit fly biological neural network) Science 10 Nov 2017: 358:6364, pp. 793-796 doi
Yves Chauvin, David E. Rumelhart (editors) Backpropagation: Theory, Architectures, and Applications, 576 pp. in series Developments in Connectionist Theory, Lawrence Erlbaum Associates 1995
Daniel Crespin, Generalized backpropagation (written for mathematicians/diff.geom.) pdf
Jean-Baptiste Bardin, Gard Spreemann, Kathryn Hess, Topological exploration of artificial neuronal network dynamics, arxiv/1810.01747
Tomasz Maszczyk, Włodzisław Duch, Support Feature Mahcines: support vectors are not enough, arxiv/1901.09643
Pankaj Mehta, David J. Schwab, An exact mapping between the variational renormalization group and deep earning, arxiv/1410.3831
A. Jakovac, D. Berenyi, P. Posfay, Understanding understanding: a renormalization group inspired model of (artificial) intelligence, arxiv/2010.13482
M. M. Bronstein et al. Geometric deep learning: going beyond Euclidean data, IEEE Signal Processing Magazine 34:4 (2017) arxiv/1611.08097 doi
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