OM DENNE EPISODE
<p>Deep Learning - Regularization Part 5</p>
<p>This video discusses multi-task learning.</p>
<p>Video References:<br />
<a href="https://www.youtube.com/channel/UCSHZKyawb77ixDdsGog4iWA">Lex Fridman's Channel</a></p>
<p>Further Reading:<br />
<a href="https://www.sciencedirect.com/science/article/pii/S093938891830120X">A gentle Introduction to Deep Learning</a></p>
<p>Links:</p>
<p><a href="https://www.springer.com/us/book/9780387310732">Link</a> - for details on Maximum A Posteriori estimation and the bias-variance decomposition<br />
<a href="https://arxiv.org/abs/1206.5533">Link</a> - for a comprehensive text about practical recommendations for regularization<br />
<a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.207.2059&rep=rep1&type=pdf">Link</a> - the paper about calibrating the variances</p>
<p>References:<br />
[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.<br />
[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.<br />
[3] Christopher M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics). Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2006.<br />
[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.<br />
[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.<br />
[6] C. Ding, C. Xu, and D. Tao. “Multi-Task Pose-Invariant Face Recognition”. In: IEEE Transactions on Image Processing 24.3 (Mar. 2015), pp. 980–993.<br />
[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.<br />
[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.<br />
[9] R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. John Wiley and Sons, inc., 2000.<br />
[10] Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. http://www.deeplearningbook.org. MIT Press, 2016.<br />
[11] Yuxin Wu and Kaiming He. “Group normalization”. In: arXiv preprint arXiv:1803.08494 (2018).<br />
[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.<br />
[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.<br />
[15] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. “Layer normalization”. In: arXiv preprint arXiv:1607.06450 (2016).<br />
[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.<br />
[17] Yoshua Bengio. “Practical recommendations for gradient-based training of deep architectures”. In: Neural networks: Tricks of the trade. Springer, 2012, pp. 437–478.<br />
[18] Chiyuan Zhang, Samy Bengio, Moritz Hardt, et al. “Understanding deep learning requires rethinking generalization”. In: arXiv preprint arXiv:1611.03530 (2016).<br />
[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].<br />
[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.<br />
[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.<br />
[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.</p>
<p> </p>
<p>This video discusses multi-task learning.</p>
<p>Video References:<br />
<a href="https://www.youtube.com/channel/UCSHZKyawb77ixDdsGog4iWA">Lex Fridman's Channel</a></p>
<p>Further Reading:<br />
<a href="https://www.sciencedirect.com/science/article/pii/S093938891830120X">A gentle Introduction to Deep Learning</a></p>
<p>Links:</p>
<p><a href="https://www.springer.com/us/book/9780387310732">Link</a> - for details on Maximum A Posteriori estimation and the bias-variance decomposition<br />
<a href="https://arxiv.org/abs/1206.5533">Link</a> - for a comprehensive text about practical recommendations for regularization<br />
<a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.207.2059&rep=rep1&type=pdf">Link</a> - the paper about calibrating the variances</p>
<p>References:<br />
[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.<br />
[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.<br />
[3] Christopher M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics). Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2006.<br />
[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.<br />
[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.<br />
[6] C. Ding, C. Xu, and D. Tao. “Multi-Task Pose-Invariant Face Recognition”. In: IEEE Transactions on Image Processing 24.3 (Mar. 2015), pp. 980–993.<br />
[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.<br />
[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.<br />
[9] R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. John Wiley and Sons, inc., 2000.<br />
[10] Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. http://www.deeplearningbook.org. MIT Press, 2016.<br />
[11] Yuxin Wu and Kaiming He. “Group normalization”. In: arXiv preprint arXiv:1803.08494 (2018).<br />
[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.<br />
[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.<br />
[15] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. “Layer normalization”. In: arXiv preprint arXiv:1607.06450 (2016).<br />
[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.<br />
[17] Yoshua Bengio. “Practical recommendations for gradient-based training of deep architectures”. In: Neural networks: Tricks of the trade. Springer, 2012, pp. 437–478.<br />
[18] Chiyuan Zhang, Samy Bengio, Moritz Hardt, et al. “Understanding deep learning requires rethinking generalization”. In: arXiv preprint arXiv:1611.03530 (2016).<br />
[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].<br />
[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.<br />
[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.<br />
[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.</p>
<p> </p>
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<p>Deep Learning - Regularization Part 4</p>
<p>This video discusses initialization techniques and transfer learning.</p>
<p>Video References:<br />
<a href="https://www.youtube.com/channel/UCSHZKyawb77ixDdsGog4iWA">Lex Fridman's Channel</a>…
<p>Deep Learning - Common Practices Part 1</p>
<p>This video discusses the use of validation data and how to choose optimizers, monitor weights, and set learning rates including their annealing.</p>
<p>Video References:<br />
<a href="https://www.youtube.com…
<p>Deep Learning - Regularization Part 3</p>
<p>This video discusses normalization such as batch normalization and self normalizing units and explains the concepts of drop-out and drop-connect</p>
<p>Video References:<br />
<a href="https://www.youtube.com/c…
<p>Deep Learning - Regularization Part 2</p>
<p>This video discusses classical regularization techniques such as early stopping using a validation set, augmentation, and maximum a-posteriori methods that expand the loss function using a regularization term.</p>
<p>Vi…
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