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Codebook Features: Sparse and Discrete Interpretability for Neural Networks

Alex Tamkin and Mohammad Taufeeque

A new interpretability method for training neural networks with sparse, discrete, and controllable hidden states.

Stanford AI Lab Papers and Talks at ICML 2023

Compiled by Drew A. Hudson

All the great work from the Stanford AI Lab accepted at ICML 2023, all in one place.

Stanford AI Lab Papers and Talks at RSS 2023

Compiled by Drew A. Hudson

All the great work from the Stanford AI Lab accepted at RSS 2023, all in one place.

Stanford AI Lab Papers and Talks at ACL 2023

Compiled by Drew A. Hudson

All the great work from the Stanford AI Lab accepted at ACL 2023, all in one place.

Self-Improving Robots: Embracing Autonomy in Robot Learning

Archit Sharma

Learning methods for robotics have a data problem. Autonomous robotic systems can deliver orders of magnitude more real-world embodied data than we have.

Stanford AI Lab Papers and Talks at CVPR 2023

Compiled by Drew A. Hudson

All the great work from the Stanford AI Lab accepted at CVPR 2023, all in one place.

Stanford AI Lab Papers and Talks at ICLR 2023

Compiled by Drew A. Hudson

All the great work from the Stanford AI Lab accepted at ICLR 2023, all in one place.

Stanford AI Lab Papers and Talks at CoRL 2022

Compiled by Drew A. Hudson

All the great work from the Stanford AI Lab accepted at CoRL 2022, all in one place.

From Discrimination in Machine Learning to Discrimination in Law, Part 1: Disparate Treatment

Fereshte Khani, Percy Liang

Stanford AI Lab Papers and Talks at EMNLP 2022

Compiled by Drew A. Hudson

All the great work from the Stanford AI Lab accepted at EMNLP 2022, all in one place.