/explore
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www.lesswrong.com | ||
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www.alignmentforum.org
A new Anthropic interpretability paper-"Toy Models of Superpostion"-came out last week that I think is quite exciting and hasn't been discussed here... |
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deepmind.google
Announcing a comprehensive, open suite of sparse autoencoders for language model interpretability. |
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transformer-circuits.pub
[AI summary] The text discusses the interpretability of features in a machine learning model, focusing on how features like Arabic, base64, and Hebrew are used in interpretable ways. It explores the extent to which these features explain the model's behavior, noting that features with higher activations are more interpretable. The text also addresses the limitations of current methods, such as the computational cost of simulating features and the potential for dataset correlations to influence feature interpretations. Finally, it concludes that the model's learning process creates a richer structure in its activations than the dataset alone, suggesting that feature-based interpretations provide meaningful insights into the model's behavior. |
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vxlabs.com
I have recently become fascinated with (Variational) Autoencoders and with PyTorch. Kevin Frans has a beautiful blog post online explaining variational autoencoders, with examples in TensorFlow and, importantly, with cat pictures. Jaan Altosaar's blog post takes an even deeper look at VAEs from both the deep learning perspective and the perspective of graphical models. Both of these posts, as well as Diederik Kingma's original 2014 paper Auto-Encoding Variational Bayes, are more than worth your time. |
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