Dashiell Stander — accepted AI-conference papers

2025

The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text

Nikhil Kandpal, Brian Lester, Colin Raffel, Sebastian Majstorovic, Stella Biderman, Baber Abbasi +21

NeurIPS 2025poster

Large language models (LLMs) are typically trained on enormous quantities of unlicensed text, a practice that has led to scrutiny due to possible intellectual property infringement and ethical concerns. Training LLMs on openly licensed text presents a first step towards addressing these issues, but…

Cited by 0 Source ↗Scholar ↗

2024

Grokking Group Multiplication with Cosets

Dashiell Stander, Qinan Yu, Honglu Fan, Stella Biderman

ICML 2024poster

The complex and unpredictable nature of deep neural networks prevents their safe use in many high-stakes applications. There have been many techniques developed to interpret deep neural networks, but all have substantial limitations. Algorithmic tasks have proven to be a fruitful test ground for int…

Cited by 11 Source ↗PDF ↗code ↗Scholar ↗

2022

VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance

Katherine Crowson, Stella Biderman, Daniel Kornis, Dashiell Stander, Eric Hallahan, Louis Castricato +1

ECCV 2022poster

"Image generation and manipulation requires technical expertise to use, inhibiting adoption. Current methods rely heavily on training to a specific domain (e.g., only faces), manual work or algorithm tuning to latent vector discovery, and manual effort in mask selection to alter only a part of an im…

Cited by 478 Source ↗PDF ↗code ↗Scholar ↗