Deep Learning State of the Art (2019) [video]

https://www.youtube.com/watch?v=53YvP6gdD7U

by ArtWomb • 8 years ago

50 5 8 years ago

sounds• 8 years ago

Was good to hear Lex Fridman's take on where we're at.

Honest question: how have people's experience with OpenAI Five been so far? I haven't had the time to check it out in detail so I'm paying close attention to what others are saying.

visarga• 8 years ago

TL;DW: BERT and BigGAN

tomahunt• 8 years ago

The main sections of the talk without note are:

- Bert and Natural Language processing, - Tesla Autopilot Hardware v2+: NN at scale, - AdaNet: AutoML with Ensembles, - AutoAugmentation, - Training Deep Networks with Synthetic data, - Segmentation Annotation with Polygon-RNN++, - DAWNBench: Training fast and cheap, - Big GAN: state of the art in image sythesis, - Video to Video Synthesis, - Semantic segmentation, - AlphaZero and openAI Five, - Deep learning frameworks

cs702• 8 years ago

Nice work. I can think of only two things that are missing:

* Normalizing flows - e.g., https://arxiv.org/abs/1605.08803 , https://arxiv.org/abs/1807.03039 , among many others

* ODEnets and continous normalizing flows - https://arxiv.org/abs/1806.07366

grej• 8 years ago

Yeah strange that ODEnets were left off, and I’m glad you mentioned it. That has the opportunity to be a transformative approach to more efficient training and much better performance on time-series problems.

Deep Learning State of the Art (2019) [video]

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