📢PSA: #NeurIPS2024 recordings are now publicly available! The workshops always have tons of interesting things on at once, so the FOMO is real😵‍💫 Luckily it's all recorded, so I've been catching up on what I missed. Thread below with some personal highlights🧵 - ThreadSky

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sedielem.bsky.social • 96 days ago

📢PSA: #NeurIPS2024 recordings are now publicly available!

The workshops always have tons of interesting things on at once, so the FOMO is real😵‍💫 Luckily it's all recorded, so I've been catching up on what I missed.

Thread below with some personal highlights🧵

Comments

sedielem.bsky.social•96 days ago

1. @davidduvenaud.bsky.social gave an inspiring talk about using language models to learn to represent functions -- the kind of thing people like to use e.g. Gaussian processes for 📈 https://neurips.cc/virtual/2024/106808

sedielem.bsky.social•96 days ago

2. A great talk from Alexis Conneau demonstrating the various challenges involved in giving LLMs a voice: https://neurips.cc/virtual/2024/106599

sedielem.bsky.social•96 days ago

3. @eerosim.bsky.social provides an in-depth look at the geometry of the distribution of natural images 🖼️ Extremely relevant to anyone trying to understand what diffusion models are really doing. https://neurips.cc/virtual/2024/109309

sedielem.bsky.social•96 days ago

4. Insights from @suryaganguli.bsky.social on creativity, generalisation and overfitting in diffusion models 🎨 https://neurips.cc/virtual/2024/107919

sedielem.bsky.social•96 days ago

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: https://neurips.cc/virtual/2024/109176

sedielem.bsky.social•96 days ago

6. @polynoamial.bsky.social talks about scaling compute at inference time, and the trade-offs involved -- in language models, but also in other settings 🧮 https://neurips.cc/virtual/2024/109258

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