Scaling Transformer to 1M tokens and beyond with RMT (Paper Explained)
Yannic Kilcher explains this paper that promises to scale transformers to 1 million tokens and beyond. We take a look at the technique behind it: The Recurrent Memory Transformer, and what its strengths and weaknesses are.
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Deep Learning New Frontiers
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Frank
- March 29, 2020
- 6.s191
- 6s191
- adversarial attacks
- AI
- Alexander Amini
- amini
- artificial intelligence
- AutoML
- ava soleimany
- bayesian deep learning
- Computer Vision
- deep evidential regression
- Deep Learning
- deep learning basics
- deep learning python
- Deep Mind
- deeplearning
- evidential deep learning
- generalization
- graph neural networks
- Introduction
- lecture 2
- Machine Learning
- MIT
- mit deep learning
- Neural Networks
- OpenAI
- soleimany
- TensorFlow
- tensorflow tutorial
- what is deep learning
MIT Introduction to Deep Learning 6.S191: Lecture 6 with Ava Soleimany. Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!! Lecture Outline 0:00 – Introduction 0:58 – Course logistics 3:59 – Upcoming guest lectures 5:35 – Deep learning and expressivity […]
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Efficient Computing for Deep Learning, Robotics, and AI
Lex Fridman shared this lecture by Vivienne Sze in January 2020 as part of the MIT Deep Learning Lecture Series. Website: https://deeplearning.mit.edu Slides: http://bit.ly/2Rm7Gi1 Playlist: http://bit.ly/deep-learning-playlist LECTURE LINKS: Twitter: https://twitter.com/eems_mit YouTube: https://www.youtube.com/channel/UC8cviSAQrtD8IpzXdE6dyug MIT professional course: http://bit.ly/36ncGam NeurIPS 2019 tutorial: http://bit.ly/2RhVleO Tutorial and survey paper: https://arxiv.org/abs/1703.09039 Book coming out in Spring 2020! OUTLINE: 0:00 – Introduction […]
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