Uploaded April 2020 | Updated September 2026, 2 weeks ago
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Please check out Machine Learning Street Talk!
youtube.com/channel/UCMLtBahI5DMrt0NPvDSoIRQ
Chip Design with Reinforcement Learning: ai.googleblog.com/2020/04/chip-design-with-deep-reinforcement.html
Jeff Dean ISSCC Keynote on The Deep Learning Revolution and its Implications of Computer Architecture and Chip Design (Highly Recommend): youtube.com/watch?v=eRlmruhTulU&t=1954s
Supervised Contrastive Learning: arxiv.org/abs/2004.11362
The Future of NLP: youtube.com/watch?v=G5lmya6eKtc
Experience Grounds Language: arxiv.org/pdf/2004.10151.pdf
The Ingredients of Real World Robotic Reinforcement Learning; https://bair.berkeley.edu/blog/2020/04/27/ingredients/
Towards Learning Robots which can Adapt: sites.google.com/view/efficient-ft/home
Adversarial Latent Autoencoders: reddit.com/r/MachineLearning/comments/g7wvpb/r_adversarial_latent_autoencoders_cvpr2020_paper
QuantNoise: ai.facebook.com/blog/training-with-quantization-noise-for-extreme-model-compression
ResNeSt: arxiv.org/pdf/2004.08955v1.pdf
Automating Data Augmentation: https://ai.stanford.edu/blog/data-augmentation/
Specification Gaming: deepmind.com/blog/article/Specification-gaming-the-flip-side-of-AI-ingenuity
Analyzing RL benchmarks with random weight guessing: arxiv.org/abs/2004.07707
PyBoy: github.com/Baekalfen/PyBoy
ICLR workshop on Slideslive: slideslive.com/iclr-2020/workshop-beyond-tabula-rasa-in-reinforcement-learning-agents-that-remember-adapt-and-generalize
Thanks for watching! Please Subscribe!
Please check out Machine Learning Street Talk!
youtube.com/channel/UCMLtBahI5DMrt0NPvDSoIRQ
Chip Design with Reinforcement Learning: ai.googleblog.com/2020/04/chip-design-with-deep-reinforcement.html
Jeff Dean ISSCC Keynote on The Deep Learning Revolution and its Implications of Computer Architecture and Chip Design (Highly Recommend): youtube.com/watch?v=eRlmruhTulU&t=1954s
Supervised Contrastive Learning: arxiv.org/abs/2004.11362
The Future of NLP: youtube.com/watch?v=G5lmya6eKtc
Experience Grounds Language: arxiv.org/pdf/2004.10151.pdf
The Ingredients of Real World Robotic Reinforcement Learning; https://bair.berkeley.edu/blog/2020/04/27/ingredients/
Towards Learning Robots which can Adapt: sites.google.com/view/efficient-ft/home
Adversarial Latent Autoencoders: reddit.com/r/MachineLearning/comments/g7wvpb/r_adversarial_latent_autoencoders_cvpr2020_paper
QuantNoise: ai.facebook.com/blog/training-with-quantization-noise-for-extreme-model-compression
ResNeSt: arxiv.org/pdf/2004.08955v1.pdf
Automating Data Augmentation: https://ai.stanford.edu/blog/data-augmentation/
Specification Gaming: deepmind.com/blog/article/Specification-gaming-the-flip-side-of-AI-ingenuity
Analyzing RL benchmarks with random weight guessing: arxiv.org/abs/2004.07707
PyBoy: github.com/Baekalfen/PyBoy
ICLR workshop on Slideslive: slideslive.com/iclr-2020/workshop-beyond-tabula-rasa-in-reinforcement-learning-agents-that-remember-adapt-and-generalize
