Princeton University

6 Posts

A Transformer Alternative Emerges: Mamba, a new approach that may outperform transformers
Princeton University

A Transformer Alternative Emerges: Mamba, a new approach that may outperform transformers

An architectural innovation improves upon transformers — up to 2 billion parameters, at least...
Language Models’ Impact on Jobs: The occupations likely to be most affected by language models
Princeton University

Language Models’ Impact on Jobs: The occupations likely to be most affected by language models

Telemarketers and college professors are most likely to find their jobs changing due to advances in language modeling, according to a new study. Researchers projected the jobs and industries in the U.S. likely to be most affected by language models.
Examples of InstaHide scrambling images
Princeton University

A Privacy Threat Revealed: How researchers cracked InstaHide for computer vision.

With access to a trained model, an attacker can use a reconstruction attack to approximate its training data. A method called InstaHide recently won acclaim for promising to make such examples unrecognizable to human eyes while retaining their utility for training.
Data and examples related to IMLE-GAN
Princeton University

Making GANs More Inclusive: A technique to help GANs represent their datasets fairly

A typical GAN’s output doesn’t necessarily reflect the data distribution of its training set. Instead, GANs are prone to modeling the majority of the training distribution, sometimes ignoring rare attributes — say, faces that represent minority populations.
Results of a technique that interprets reflected light to reveal objects outside the line of sight
Princeton University

Periscope Vision: Researchers used deep learning to see around corners.

Wouldn’t it be great to see around corners? Deep learning researchers are working on it. Researchers developed deep-inverse correlography, a technique that interprets reflected light to reveal objects outside the line of sight.
ImageNet face recognition labels on a picture
Princeton University

ImageNet Gets a Makeover: The effort to remove bias from ImageNet

Computer scientists are struggling to purge bias from one of AI’s most important datasets. ImageNet’s 14 million photos are a go-to collection for training computer-vision systems, yet their descriptive labels have been rife with derogatory and stereotyped attitudes toward race, gender, and sex.

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