'How neural networks learn' - Part III: Generalization and Overfitting

In this third episode on "How neural nets learn" I dive into a bunch of academical research that tries to explain why neural networks generalize as wel as they do. We first look at the remarkable capability of DNNs to simply memorize huge amounts of (random) data. We then see how this picture is more subtle when training on real data and finally dive into some beautiful analysis from the viewpoint on information theory. Main papers discussed in this video: First paper on Memorization in DNNs: https://arxiv.org/abs/1611.03530 A closer look at memorization in Deep Networks: https://arxiv.org/abs/1706.05394 Opening the Black Box of Deep Neural Networks via Information: https://arxiv.org/abs/1703.00810 Other links: Quanta Magazine blogpost on Tishby's work: https://www.quantamagazine.org/new-theory-cracks-open-the-black-box-of-deep-learning-20170921/ Tishby's lecture at Stanford: https://youtu.be/XL07WEc2TRI Amazing lecture by Ilya Sutkever at MIT: https://youtu.be/9EN_HoEk3KY If you want to support this channel, here is my patreon link: https://patreon.com/ArxivInsights --- You are amazing!! ;) If you have questions you would like to discuss with me personally, you can book a 1-on-1 video call through Pensight: https://pensight.com/x/xander-steenbrugge