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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.

An Introduction to Knowledge Graphs

Vinay K. Chaudhri, Naren Chittar, Michael Genesereth

What are Knowledge Graphs and how are they useful in search, data integration and AI?

Stanford AI Lab Papers and Talks at ICLR 2021

Compiled by Nelson Liu

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

Conventions in Multi-Agent Collaboration

Andy Shih

Learning convention-aware agents for adaptive multi-agent collaboration

Broadening the Reach of Contrastive Learning with Viewmaker Networks

Alex Tamkin

A single self-supervised algorithm for pretraining on many different modalities

Stanford AI Lab Papers and Talks at AISTATS 2021

Compiled by Megha Srivastava

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

Inside Chirpy Cardinal: Stanford's Open-Source Social Chatbot that Won 2nd place in the Alexa Prize

Ashwin Paranjape and Amelia Hardy

Last year, Stanford won 2nd place in the Alexa Prize Socialbot Grand Challenge for social chatbots. In this post, we look into building a chatbot that combines the flexibility and naturalness of neural dialog generation with the reliability and practicality of scripted dialogue.

Neural Mechanics: Symmetry and Broken Conservation Laws In Deep Learning Dynamics

Daniel Kunin and Hidenori Tanaka

By exploiting architectural symmetry, our work demonstrates that we can analytically describe the learning dynamics of various parameter combinations at finite learning rates and batch sizes for state of the art architectures trained on any dataset.