Neurips2022-Foundational Robustness of Foundation Models
FreeNeurIPS 2022 Tutorial: Foundational Robustness of Foundation Models
FreeFree tier
About Neurips2022-Foundational Robustness of Foundation Models
This NeurIPS 2022 tutorial provides a comprehensive overview of the foundational robustness of foundation models. Led by Pin-Yu Chen, Sijia Liu, and Sayak Paul, it includes lectures on basics and deep dives into computer vision and code models, a hands-on Jupyter/Colab live coding demo, and a panel discussion on trustworthiness aspects such as robustness and privacy.
Key Features
Comprehensive lectures on foundation model robustness
Deep dives on computer vision and code models
Hands-on Jupyter/Colab live coding demo
Panel discussion on trustworthiness and privacy
Organized by Pin-Yu Chen, Sijia Liu, Sayak Paul
Pros & Cons
Pros
- Free access to expert-led tutorial
- Covers both theory and practical coding
- Includes panel with multiple researchers
Cons
- Content may be dated (2022)
- Not a standalone tool, but an educational resource
Best For
Learning about robustness in foundation modelsUnderstanding privacy risks in large-scale modelsGaining hands-on experience with robustness evaluation
FAQ
What topics are covered in this tutorial?
The tutorial covers basics of robustness in foundation models, deep dives on computer vision and code models, and a panel discussion on trustworthiness aspects such as robustness and privacy.
Is there a hands-on component?
Yes, the tutorial includes a hands-on Jupyter Notebook/Colab walkthrough led by Sayak Paul.