This is a tutorial on common practices in training generative models that optimize likelihood directly, such as autoregressive models and normalizing flows. Deep generative modeling is a fast-moving field, so I hope for this to be a newcomer-friendly …

In my earlier post on meta-learning, the problem is mainly defined in the context of few-shot classification. Here I would like to explore more into cases when we try to “meta-learn” Reinforcement Learning (RL) tasks by developing an agent…

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We search for neural network architectures that can already perform various tasks even when they use random weight values.
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Redirecting to weightagnostic.github.io, where the article resides.

Translations: 中文

I’ve been at Google Brain robotics (now referred to as Robotics @ Google) for nearly 3 years. It’s helpful to reflect, from time to time, on the scientific, engineering and personal productivity takeaways gleaned from w…