Deep learning: theory and practice

• Autorzy: Cichocki A. , Poggio T. , Osowski S. , Lempitsky V.

Identyfikatory

DOI

10.24425/bpas.2018.125923

• Języki publikacji: EN

Abstrakty

EN

This Special Section of the Bulletin of the Polish Academy of Sciences on Technical Sciences is devoted to theoretical aspects of deep machine learning as well as practical applications in some areas of signal and image processing, particularly in bioengineering.

Słowa kluczowe

EN

deep learning; networks; theory; practice

PL

uczenie głębokie; sieci; teoria; praktyka

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2018
• Tom: Vol. 66, nr 6
• Strony: 757--759
• Opis fizyczny: Bibliogr. 12 poz.

Twórcy

autor

Cichocki A.

• Warsaw University of Technology, Poland

autor

Poggio T.

• Eugene McDermott Professor at the Department of Brain & Cognitive Sciences at MIT and the director of the new NSF Center for Brains, Minds and Machines at MIT

autor

Osowski S.

• M.Sc., Ph.D. and D.Sc. degrees from the Warsaw University of Technology, Poland

autor

Lempitsky V.

• Samsung AI Center in Moscow and an Associate Professor at Skoltech (Skolkovo Institute of Science and Technology)

Bibliografia

• [1] T. Poggio, H. Mhaskar, L. Rosasco, B. Miranda, and Q. Liao, “Theory I: networks and the curse of dimensionality”, Bull. Pol. Ac.: Tech. 66(6), 761‒774 (2018).
• [2] T. Poggio and Q. Liao, “Theory II: Deep learning and optimization”, Bull. Pol. Ac.: Tech. 66(6), 775‒788 (2018).
• [3] A. Novikov, M. Trofimov, and I. Oseledets, “Exponential machines”, Bull. Pol. Ac.: Tech. 66(6), 789‒798 (2018).
• [4] V. Lebedev and V. Lempitsky, “Speeding-up convolutional neural networks: A survey”, Bull. Pol. Ac.: Tech. 66(6), 799‒810 (2018).
• [5] M. Figurnov, A. Sobolev, and D. Vetrov, “Probabilistic adaptive computation time”, Bull. Pol. Ac.: Tech. 66(6), 811‒820 (2018).
• [6] F. Horn and K.R. Müller, “Predicting pairwise relations with neural similarity encoders”, Bull. Pol. Ac.: Tech. 66(6), 821‒830 (2018).
• [7] J. Kurek, B. Świderski, S. Osowski, M. Kruk, and W. Barhoumi, “Deep learning versus classical neural approach to mammogram recognition”, Bull. Pol. Ac.: Tech. 66(6), 831‒840 (2018).
• [8] Y. Qiu, G. Zhou, Q. Zhao, and A. Cichocki, “Comparative study on the classification methods for breast cancer diagnosis”, Bull. Pol. Ac.: Tech. 66(6), 841‒848 (2018).
• [9] Z. Swiderska-Chadaj, T. Markiewicz, J. Gallego, G. Bueno, B. Grala, and M. Lorent, “Deep learning for damaged tissue detection and segmentation in Ki-67 brain tumor specimens based on the U-net model”, Bull. Pol. Ac.: Tech. 66(6), 849‒856 (2018).
• [10] B. Stasiak, P. Tarasiuk, I. Michalska, and A. Tomczyk, “Application of convolutional neural networks with anatomical knowledge for brain MRI analysis in MS patients”, Bull. Pol. Ac.: Tech. 66(6), 857‒868 (2018).
• [11] J. Jakubowski and J. Chmielińska, “Detection of driver fatigue symptoms using transfer learning”, Bull. Pol. Ac.: Tech. 66(6), 869‒874 (2018).
• [12] V. Osin, A. Cichocki, and E. Burnaev, “Fast multispectral deep fusion networks”, Bull. Pol. Ac.: Tech. 66(6), 875‒890 (2018).

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).