Foundation models adopting the methodology of deep learning with pre-training on large-scale unlabeled data and finetuning with task-specific supervision are becoming a mainstream technique in machine learning. Although foundation models hold many promises in learning general representations and few-shot/zero-shot generalization across domains and data modalities, at the same time they raise unprecedented challenges and considerable risks in robustness and privacy due to the use of the excessive volume of data and complex neural network architectures. This tutorial aims to deliver a Coursera-like online tutorial containing comprehensive lectures, a hands-on and interactive Jupyter/Colab live coding demo, and a panel discussion on different aspects of trustworthiness in foundation models.

• NeurIPS Tutorial Webpage: <FRFM-NeurIPS>

• Video Recording: <FRFM-Video>

• Tutorial Slides: <FRFM-slides>

• Code and slides are made available here: <bit.ly/nips-22-content>

Speakers:

Outline:

1. Basics in foundation models and robustness

2. Deep dive on foundation models for computer vision

3. Deep dive on foundation models for code

4. Hands-on code walkthrough

5. Concluding Remarks

6. Q&A

7. Panel discussion

Panelists:

Contributors to code demos