Zoran Skoda machine learning


Software platforms

Early references

Pioneering research:

  • M. L. Minsky, S. A. Papert, Perceptrons, Cambridge, MA: MIT Press, 1969
  • B. E. Boser, I. M. Guyon, V. N. Vapnik, A training algorithm for optimal margin classifiers, In: In D. Haussler, editor, 5th Annual ACM Workshop on COLT, 144-152, ACM Press. (1992)

Learning and teaching theory

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.

  • Xiaojin Zhu, Adish Singla, Sandra Zilles, Anna N. Rafferty, An overview of machine teaching, arxiv/1801.05927
  • SA Goldman, MJ Kearns, On the complexity of teaching, pdf 1995
  • Steve Hanneke, Theory of active learning, pdf
  • Shai Shalev-Shwartz, Shai Ben-David, Understanding machine learning: from theory to algorithms, webpage Cambridge University Press 2014
  • Roman Vershynin, High-dimensional probability: an introduction with applications in data science, bookdraft, pdf
  • Andreas Krause, Daniel Golovin, Submodular function maximization, survey, pdf
  • Shai Shalev-Shwartz, Online learning and online convex optimization, Foundations and trends in machine learning, 4:2, 107-194 (2011) doi pdf
  • Sébastien Bubeck, Nicolò Cesa-Bianchi, Regret analysis of stochastic and nonstochastic multi-armed bandit problems, Foundations and trends in machine learning 5:1 (2012) 1–122 pdf arxiv/1204.5721 doi
  • А.Я. Червоненкис, Теория обучения машин, Курс лекций в Школе Анализа Данных (Яндекс-школе), 22 лекции по методам машинного обучения и распознаванию образов
  • В. Н. Вапник, А. Я. Червоненкис, Теория равномерной сходимости частот появления событий к их вероятностям и задачи поиска оптимального решения по эмпирическим данным // Автоматика и телемеханика. — 1971. — Т. 32, № 2. — С. 207—317. — ISSN 0005-2310 pdf
  • Вапник В. Н., Червоненкис А. Я. Теория распознавания образов. М.: Наука, 1974
  • V. Vapnik, An overview of statistical learning theory - Neural Networks, IEEE trans. on neural networks, 10:5 (1999) pdf
  • V. N. Vapnik, Statistical Learning Theory, 2nd. ed. John-Wiley and Sons. New York, 1998
  • S. Amari, A theory of adaptive pattern classifiers, IEEE Transactions on Electronic Computers, EC-16(3), 299–307 doi
  • Matus Telgarsky, Benefits of depth in neural networks, arxiv.org/1602.04485
  • F. Cucker, S. Smale, On the mathematical foundations of learning, Bull. AMS 39:1, 1–49, 2002 pdf mirror
  • S. Ben-David, P. Hrubes, S. Moran, A. Yehudayoff, Learnability can be undecidable, Nature Machine intelligence, 1 (2019) 44-48.

Categories, (tropical, information etc.) geometry versus machine learning

  • Jared Culbertson, Kirk Sturtz, Bayesian machine learning via category theory, arxiv/1312.1445
  • Liwen Zhang, Gregory Naitzat, Lek-Heng Lim, Tropical geometry of deep neural networks, arxiv/1805.07091
  • Shun-ichi Amari, Ryo Karakida, Masafumi Oizumi, Fisher information and natural gradient learning of random deep networks, arxiv/1808.07172


Some notable people and institutions

General and conceptual

  • 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

  • FFX shallow algorithm

  • 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

With specific kinds of applications

  • Emmanuel de Bezenac, Arthur Pajot, Patrick Gallinari, Deep learning for physical processes: incorporating prior scientific knowledge, arxiv/1711.07970
  • Ryan Ferguson, Andrew Green, Applying deep learning to derivatives valuation, arxiv/1809.02233
  • Yurui Qu, Li Jing, Yichen Shen, Min Qiu, Marin Soljacic, Migrating knowledge between physical scenarios based on artificial neural networks, https://arxiv/1809.00972; Li Jing et al. Tunable efficient unitary neural networks (EUNN) and their application to RNNs, https://arxiv/1612.05231
  • Lorenzo Pastori, Raphael Kaubruegger, Jan Carl Budich, Generalized transfer matrix states from artificial neural networks, arxiv/1808.02069 (wavefunctions)
  • Jaideep Pathak, Alexander Wikner, Rebeckah Fussell, Sarthak Chandra, Brian Hunt, Michelle Girvan, Edward Ott, Hybrid forecasting of chaotic processes: using machine learning in conjunction with a knowledge-based model, arxiv/1803.04779 and related popular article in quantamagazine
  • Abigail See, Four deep learning trends from ACL2017 (comprehensive conference report in blog form)

Last revised on December 7, 2020 at 13:22:59. See the history of this page for a list of all contributions to it.