Foundation Models

Online Lectures, 2022

Overview: This course aims to gather together some of the key ideas behind Foundation Models through detailed discussions recent research papers on this topic. All lectures are publicly available on YouTube. Links to the videos and slides can be found below.

1. Introduction: On the Opportunities and Risks of Foundation Models


Notes: A video description of the ideas covered in the introductory section of the work "On the Opportunities and Risks of Foundation Models" by R. Bommasani et al. published on arxiv in 2021. This paper can be found on arxiv here.
Slides: link (pdf)
Further resources and references: link

2. GPT-3: Language Models are Few-shot Learners


Notes: A video description of the paper "Language Models are Few-shot Learners" by T. Brown et al. published at NeurIPS 2020, which introduced the GPT-3 model. The paper can be found on arxiv here.
Slides: link (pdf)
Further resources and references: link

3. CLIP: Contrastive Language-Image Pre-training


Notes: A video description of the paper "Learning transferable visual models from natural language supervision" by A. Radford et al. published at ICML 2021, which introduced the CLIP family of models. The paper can be found on arxiv here.
Slides: link (pdf)
Further resources and references: link

4. DINO: Emerging properties in self-supervised vision transformers


Notes: A video description of the DINO framework, introduced in the work "Emerging properties in self-supervised vision transformers" by M. Caron, H. Touvron, I. Misra, H. J├ęgou, J. Mairal, P. Bojanowski and A. Joulin, published at ICCV in 2021. This paper can be found on arxiv here. Code and models can be found here.
Slides: link (pdf)
Further resources and references: link

5. Flamingo: a Visual Language Model for Few-Shot Learning


Notes: A video description of the paper "Flamingo: a Visual Language Model for Few-Shot Learning" by J-B. Alayrac and co-authors, posted on arxiv in April 2022. This paper, which introduced the Flamingo family of models, can be found on arxiv here.
Slides: link (pdf)
Further resources and references: link

6. Codex: Evaluating Large Language Models Trained on Code


Notes: A video description of the paper "Evaluating Large Language Models Trained on Code" by M. Chen and co-authors, posted on arxiv in July 2021. This paper, which introduced the Codex family of models for code generation, can be found on arxiv here.
Slides: link (pdf)
Further resources and references: link

Note about course content: As noted in the first lecture above, the topic, definition or indeed the name "Foundation Models" is not yet agreed upon in the research community (and may change). The course is a work-in-progress.