... also very honoured and grateful to see my blog linked in the video description! 🥹🙏🙇

Everyone is welcome!

Note also that getting this number slightly wrong isn't that big a deal. Even if you make it 100k instead of 10k, it's not going to change the granularity of the high frequencies that much because of the logarithmic frequency spacing.

The frequencies are log-spaced, so historically, 10k was plenty to ensure that all positions can be uniquely distinguished. Nowadays of course sequences can be quite a bit longer.

Yes! Also listen to this and contemplate the universe: grumusic.bandcamp.com/album/cosmog...

This is just a tiny fraction of what's available, check out the schedule for more: neurips.cc/virtual/2024...

10. Last but not least (😎), here's my own workshop talk about multimodal iterative refinement: the methodological tension between language and perceptual modalities, autoregression and diffusion, and how to bring these together 🍸 neurips.cc/virtual/2024...

9. A great overview of various strategies for merging multiple models together by Colin Raffel 🪿 neurips.cc/virtual/2024...

8. Ishan Misra gives a nice overview of Meta's Movie Gen model 📽️ (I have some questions about the diffusion vs. flow matching comparison though😁) neurips.cc/virtual/2024...

7. More on test-time scaling from @tomgoldstein.bsky.social, using a different approach based on recurrence 🐚 neurips.cc/virtual/2024... (some interesting comments on the link with diffusion models in the questions at the end!)

6. @polynoamial.bsky.social talks about scaling compute at inference time, and the trade-offs involved -- in language models, but also in other settings 🧮 neurips.cc/virtual/2024...

5. Sparse autoencoders were in vogue well over a decade ago, back when I was doing my PhD. They've recently been revived in the context of mechanistic interpretability of LLMs 🔍 @neelnanda.bsky.social gives a nice overview: neurips.cc/virtual/2024...