This page collects papers, books, articles, videos, and other resources suggested by our community for potential future discussions. We welcome suggestions that align with our focus on impactful and seminal machine learning content.
Current Suggestions
- Hongsup: AI as Normal Technology
This section will be populated with new paper suggestions as our journal club resumes regular meetings.
How to Suggest Papers
Have a paper, book chapter, news article, lecture, or other resource you think would spark great discussion? You can: - Contact the organizer directly - Submit a suggestion via GitHub issue - Bring it up during our monthly meetings
We look for content that is impactful, thought-provoking, or offers practical insights into how machine learning works in practice, regardless of format or technical complexity.
Previously Suggested Papers
The following were suggested by community members during our earlier meeting phases:
- Solving olympiad geometry without human demonstrations shared by Hongsup
- Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training shared by Kshitij
- TOFU: A Task of Fictitious Unlearning for LLMs shared by Sarah
- Quantifying the impact of uninformative features on the performance of supervised classification and dimensionality reduction algorithms shared by Kshitij
- Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To! shared by Hongsup
- A Mulching Proposal shared by Hongsup
- Evaluating and Mitigating Discrimination in Language Model Decisions shared by Kshitij
- Dive into Deep Learning: Coding Session #4 Attention Mechanism I (MLT Artificial Intelligence) shared by Kshitij
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks shared by Brian
- MiniLLM: Large Language Models on Consumer GPUs shared by Hongsup
- The TinyLlama project shared by Kshitij
- On the Opportunities and Risks of Foundation Models shared by Hongsup
- Challenges in Deploying Machine Learning: a Survey of Case Studies shared by Hongsup
- Machine Learning and the Future of Bayesian Computation shared by Brian