Refining the source of adversarial examples

Section 3.2 of Ilyas et al. (2019) shows that training a model on only adversarial errors leads to non-trivial generalization on the original test set. We show that these experiments are a specific case of learning from errors.

JAX is a great linear algebra + automatic differentiation library for fast experimentation with and teaching machine learning. Here is a lightweight example, in just 75 lines of JAX, of how to implement Real-NVP.

This post is based off of a …

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.