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Deep Learning State of the Art (2019) [video]
- sounds 8y agoWas 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 8y agoTL;DW: BERT and BigGAN
- tomahunt 8y agoThe 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 8y agoNice 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/1605.08803 , https://arxiv.org/abs/1807.03039 https://arxiv.org/abs/1807.03039 , among many others * ODEnets and continous normalizing flows - https://arxiv.org/abs/1806.07366 https://arxiv.org/abs/1806.07366
- grej 8y agoYeah 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.