25 - Deep Learning - Plain Version 2020/ClipID:21121 previous clip next clip

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Recording date 2020-10-12

Via

Free

Language

English

Organisational Unit

Lehrstuhl für Informatik 5 (Mustererkennung)

Producer

Lehrstuhl für Informatik 5 (Mustererkennung)

Format

lecture

Deep Learning - Common Practices Part 4

This video discusses how to evaluate deep learning approaches.

For reminders to watch the new video follow on Twitter or LinkedIn.

Further Reading:
A gentle Introduction to Deep Learning

References:
[1] M. Aubreville, M. Krappmann, C. Bertram, et al. “A Guided Spatial Transformer Network for Histology Cell Differentiation”. In: ArXiv e-prints (July 2017). arXiv: 1707.08525 [cs.CV].
[2] James Bergstra and Yoshua Bengio. “Random Search for Hyper-parameter Optimization”. In: J. Mach. Learn. Res. 13 (Feb. 2012), pp. 281–305.
[3] Jean Dickinson Gibbons and Subhabrata Chakraborti. “Nonparametric statistical inference”. In: International encyclopedia of statistical science. Springer, 2011, pp. 977–979.
[4] Yoshua Bengio. “Practical recommendations for gradient-based training of deep architectures”. In: Neural networks: Tricks of the trade. Springer, 2012, pp. 437–478.
[5] Chiyuan Zhang, Samy Bengio, Moritz Hardt, et al. “Understanding deep learning requires rethinking generalization”. In: arXiv preprint arXiv:1611.03530 (2016).
[6] Boris T Polyak and Anatoli B Juditsky. “Acceleration of stochastic approximation by averaging”. In: SIAM Journal on Control and Optimization 30.4 (1992), pp. 838–855.
[7] Prajit Ramachandran, Barret Zoph, and Quoc V. Le. “Searching for Activation Functions”. In: CoRR abs/1710.05941 (2017). arXiv: 1710.05941.
[8] Stefan Steidl, Michael Levit, Anton Batliner, et al. “Of All Things the Measure is Man: Automatic Classification of Emotions and Inter-labeler Consistency”. In: Proc. of ICASSP. IEEE - Institute of Electrical and Electronics Engineers, Mar. 2005.

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