In this paper, we leverage large language models (LMs) to perform zero-shot text style transfer.
As neural language models grow in effectiveness, they are increasingly being applied in real-world settings.
Interpretability of machine learning models is critical to scientific understanding, AI safety, as well as debugging.
We present the Language Interpretability Tool (LIT), an open-source platform for visualization and understanding of NLP models.
The Gestalt laws of perceptual organization, which describe how visual elements in an image are grouped and interpreted, have traditionally been thought of as innate despite their ecological validity.
no code implementations • 30 Jan 2019 • Narayan Hegde, Jason D. Hipp, Yun Liu, Michael E. Buck, Emily Reif, Daniel Smilkov, Michael Terry, Carrie J. Cai, Mahul B. Amin, Craig H. Mermel, Phil Q. Nelson, Lily H. Peng, Greg S. Corrado, Martin C. Stumpe
SMILY may be a useful general-purpose tool in the pathologist's arsenal, to improve the efficiency of searching large archives of histopathology images, without the need to develop and implement specific tools for each application.
Embeddings are ubiquitous in machine learning, appearing in recommender systems, NLP, and many other applications.