Deep Learning - Regularization Part 5

This video discusses multi-task learning.

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Video References:
Lex Fridman's Channel

Further Reading:
A gentle Introduction to Deep Learning

Links:

Link - for details on Maximum A Posteriori estimation and the bias-variance decomposition
Link - for a comprehensive text about practical recommendations for regularization
Link - the paper about calibrating the variances

References:
[1] Sergey Ioffe and Christian Szegedy. “Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift”. In: Proceedings of The 32nd International Conference on Machine Learning. 2015, pp. 448–456.
[2] Jonathan Baxter. “A Bayesian/Information Theoretic Model of Learning to Learn via Multiple Task Sampling”. In: Machine Learning 28.1 (July 1997), pp. 7–39.
[3] Christopher M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics). Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2006.
[4] Richard Caruana. “Multitask Learning: A Knowledge-Based Source of Inductive Bias”. In: Proceedings of the Tenth International Conference on Machine Learning. Morgan Kaufmann, 1993, pp. 41–48.
[5] Andre Esteva, Brett Kuprel, Roberto A Novoa, et al. “Dermatologist-level classification of skin cancer with deep neural networks”. In: Nature 542.7639 (2017), pp. 115–118.
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[7] Li Wan, Matthew Zeiler, Sixin Zhang, et al. “Regularization of neural networks using drop connect”. In: Proceedings of the 30th International Conference on Machine Learning (ICML-2013), pp. 1058–1066.
[8] Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, et al. “Dropout: a simple way to prevent neural networks from overfitting.” In: Journal of Machine Learning Research 15.1 (2014), pp. 1929–1958.
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[11] Yuxin Wu and Kaiming He. “Group normalization”. In: arXiv preprint arXiv:1803.08494 (2018).
[12] Kaiming He, Xiangyu Zhang, Shaoqing Ren, et al. “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification”. In: Proceedings of the IEEE international conference on computer vision. 2015, pp. 1026–1034.
[13] D Ulyanov, A Vedaldi, and VS Lempitsky. Instance normalization: the missing ingredient for fast stylization. CoRR abs/1607.0 [14] Günter Klambauer, Thomas Unterthiner, Andreas Mayr, et al. “Self-Normalizing Neural Networks”. In: Advances in Neural Information Processing Systems (NIPS). Vol. abs/1706.02515. 2017. arXiv: 1706.02515.
[15] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. “Layer normalization”. In: arXiv preprint arXiv:1607.06450 (2016).
[16] Nima Tajbakhsh, Jae Y Shin, Suryakanth R Gurudu, et al. “Convolutional neural networks for medical image analysis: Full training or fine tuning?” In: IEEE transactions on medical imaging 35.5 (2016), pp. 1299–1312.
[17] Yoshua Bengio. “Practical recommendations for gradient-based training of deep architectures”. In: Neural networks: Tricks of the trade. Springer, 2012, pp. 437–478.
[18] Chiyuan Zhang, Samy Bengio, Moritz Hardt, et al. “Understanding deep learning requires rethinking generalization”. In: arXiv preprint arXiv:1611.03530 (2016).
[19] Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, et al. “How Does Batch Normalization Help Optimization?” In: arXiv e-prints, arXiv:1805.11604 (May 2018), arXiv:1805.11604. arXiv: 1805.11604 [stat.ML].
[20] Tim Salimans and Diederik P Kingma. “Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks”. In: Advances in Neural Information Processing Systems 29. Curran Associates, Inc., 2016, pp. 901–909.
[21] Xavier Glorot and Yoshua Bengio. “Understanding the difficulty of training deep feedforward neural networks”. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence 2010, pp. 249–256.
[22] Zhanpeng Zhang, Ping Luo, Chen Change Loy, et al. “Facial Landmark Detection by Deep Multi-task Learning”. In: Computer Vision – ECCV 2014: 13th European Conference, Zurich, Switzerland, Cham: Springer International Publishing, 2014, pp. 94–108.