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Meta-Learning Student Feedback to 16,000 Solutions

A meta-learning system for automatic feedback to student code education with applications to university level courses and a large scale online learning platform.

WILDS: A Benchmark of in-the-Wild Distribution Shifts

Shiori Sagawa and Pang Wei Koh

A curated benchmark of 10 datasets with real-world distribution shifts.

Stanford AI Lab Papers and Talks at ICML 2021

Compiled by Megha Srivastava

All the great work from the Stanford AI Lab accepted at ICML 2021, all in one place!

Reasoning with Language Models and Knowledge Graphs for Question Answering

Michihiro Yasunaga

We introduce an end-to-end question answering model, QA-GNN, that jointly reasons with pre-trained language models and knowledge graphs.

AGQA: A Benchmark for Compositional, Spatio-Temporal Reasoning

Madeleine Grunde-McLaughlin, Ranjay Krishna, and Maneesh Agrawala

Take a look at the video above and the associated question – What did they hold before opening the closet?. After looking at the video, you can...

Stanford AI Lab Papers and Talks at CVPR 2021

Compiled by Nelson Liu

All the great work from the Stanford AI Lab accepted at CVPR 2021, all in one place.

Extrapolating to Unnatural Language Processing with GPT-3's In-context Learning: The Good, the Bad, and the Mysterious

Frieda Rong

In this post, we examine in-context learning in GPT-3 through a series of experiments dissecting GPT-3's ability to extrapolate to less natural inputs.