Quantifying and Understanding Memorization in Deep Neural Networks Published 2023-03-22 Download video MP4 360p Recommendations 18:40 But what is a neural network? | Chapter 1, Deep learning 1:27:21 CBMM10 Panel: Research on Intelligence in the Age of AI 1:00:42 An overview of Generative AI: music, video and image creation 15:05 Variational Autoencoders 59:06 Navigating Progress in AI and Neuroscience 55:15 MIT 6.S191: Convolutional Neural Networks 1:08:47 MIT 6.S191: Deep Learning New Frontiers 55:42 Spiking Neural Networks for More Efficient AI Algorithms 59:52 MIT 6.S191: Deep Generative Modeling 21:02 The Attention Mechanism in Large Language Models 56:26 Dense Associative Memory in Machine Learning 50:43 12a: Neural Nets 15:14 How are memories stored in neural networks? | The Hopfield Network #SoME2 38:27 ICLR 2021 Keynote - "Geometric Deep Learning: The Erlangen Programme of ML" - M Bronstein 58:12 MIT Introduction to Deep Learning | 6.S191 16:37 Recurrent Neural Networks (RNNs), Clearly Explained!!! 16:27 An introduction to Reinforcement Learning 1:42:18 Intro to Machine Learning & Neural Networks. How Do They Work? 36:15 Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!! 20:33 Gradient descent, how neural networks learn | Chapter 2, Deep learning Similar videos 34:31 Memorization in Machine Learning 55:54 Chasing the Long Tail: What Neural Networks Memorize and Why 21:15 Understanding Deep Learning (Still) Requires Rethinking Generalization (Paper Breakdown) 1:13:32 Raphaël Millière: Compositionality in Deep Neural Networks | Philosophy of Deep Learning 1:18:23 CRML Distinguished Lecture: Adam Smith | When is Memorization Necessary for Machine Learning? 34:36 On memorization in neural networks (& a small exploration of memorization in ChatGPT) 1:15:38 Lecture 5 - Deep Learning Foundations: deep learning generalization 46:38 Paper Reading & Discussion: Quantifying Memorization Across Neural Language Models 2:00:31 Building your First Neural Network 52:52 MIT 6.S191 (2020): Introduction to Deep Learning 04:04 Neural network quantization with AdaRound 2:09:17 Understand & Improve Memory Using Science-Based Tools | Huberman Lab Podcast #72 05:00 Dataless Model Selection With the Deep Frame Potential 2:56:56 Interpretability | Interpretability and Analysis in Neural NLP 59:15 When is Memorization of Entire Examples Necessary for High-Accuracy Learning? 54:20 Embracing Change: Continual Learning in Deep Neural Networks More results