Search Results for author: Mitchell Gordon

Found 5 papers, 2 papers with code

Localizing Paragraph Memorization in Language Models

1 code implementation28 Mar 2024 Niklas Stoehr, Mitchell Gordon, Chiyuan Zhang, Owen Lewis

Can we localize the weights and mechanisms used by a language model to memorize and recite entire paragraphs of its training data?

Language Modelling Memorization

A Roadmap to Pluralistic Alignment

1 code implementation7 Feb 2024 Taylor Sorensen, Jared Moore, Jillian Fisher, Mitchell Gordon, Niloofar Mireshghallah, Christopher Michael Rytting, Andre Ye, Liwei Jiang, Ximing Lu, Nouha Dziri, Tim Althoff, Yejin Choi

We identify and formalize three possible ways to define and operationalize pluralism in AI systems: 1) Overton pluralistic models that present a spectrum of reasonable responses; 2) Steerably pluralistic models that can steer to reflect certain perspectives; and 3) Distributionally pluralistic models that are well-calibrated to a given population in distribution.

Visual Intelligence through Human Interaction

no code implementations12 Nov 2021 Ranjay Krishna, Mitchell Gordon, Li Fei-Fei, Michael Bernstein

Over the last decade, Computer Vision, the branch of Artificial Intelligence aimed at understanding the visual world, has evolved from simply recognizing objects in images to describing pictures, answering questions about images, aiding robots maneuver around physical spaces and even generating novel visual content.

Approximating Human Judgment of Generated Image Quality

no code implementations30 Nov 2019 Y. Alex Kolchinski, Sharon Zhou, Shengjia Zhao, Mitchell Gordon, Stefano Ermon

Generative models have made immense progress in recent years, particularly in their ability to generate high quality images.

HYPE: Human-eYe Perceptual Evaluation of Generative Models

no code implementations ICLR Workshop DeepGenStruct 2019 Sharon Zhou, Mitchell Gordon, Ranjay Krishna, Austin Narcomey, Durim Morina, Michael S. Bernstein

The second, HYPE-Infinity, measures human error rate on fake and real images with no time constraints, maintaining stability and drastically reducing time and cost.

Image Generation Unconditional Image Generation

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