Diffusion models have connections to multiple types of generative models. The previous resources talk about the score-based model connection, this one connects diffusion models to VAEs.
As part of his amazing "Introduction to deep generative modeling" blog series, Dr. Jakub Tomczak provides a great intro and code examples of diffusion models.
After going through the introductory resources shared here, reading the original papers will be quite informative too!
The DDPM paper was the breakout diffusion model paper.
I have tried to include a diversity of resources that provide different perspectives on diffusion models. Hopefully it provides you different ways about thinking and learning about the topic!
If you like this thread, please share! 🙏
I am also working on my own blog post about diffusion models 👀
A new startup, Inception Labs, has released Mercury Coder, "the first commercial-scale diffusion large language model"
It's 5-10x faster than current gen LLMs, providing high-quality responses at low costs.
And you can try it now!
The performance is similar to small frontier models while achieving a throughput of ~1000 tokens/sec... on H100s! Reaching this level of throughput for autoregressive LLMs typically requires specialized chips.
It's currently tied for second place on Copilot Arena!
Cleo was an account on Math Stack Exchange that was infamous for dropping the answer to the most difficult integrals with no explanation...
often mere minutes after the question was asked!!
For years, no one knew who Cleo was, UNTIL NOW!
People noticed that the same few people were interacting with Cleo (asking the questions Cleo answered, commenting, etc.), a couple of them only active at the same time as Cleo as well.
People were wondering maybe someone is controlling all these accounts as alts
One of the accounts, Laila Podlesny, had an email address associated with it, and by trying to fake log into the Gmail and obtaining the backup recovery email, someone figured out that Vladimir Reshetnikov was in control of Laila Podlesny.
Based on other ineractions from Vladimir on Math.SE, it seemed likely he controlled Cleo, Laila, and couple other accounts as well.
This a diffusion model pipeline that goes beyond what AlphaFold2 did: predicting the structures of protein-molecule complexes containing DNA, RNA, ions, etc.
Google announces Med-Gemini, a family of Gemini models fine-tuned for medical tasks! 🔬
Achieves SOTA on 10 of the 14 benchmarks, spanning text, multimodal & long-context applications.
Surpasses GPT-4 on all benchmarks!
This paper is super exciting, let's dive in ↓
The team developed a variety of model variants. First let's talk about the models they developed for language tasks.
The finetuning dataset is quite similar to Med-PaLM2, except with one major difference:
self-training with search
(2/14)
The goal is to improve clinical reasoning and ability to use search results.
Synthetic chain-of-thought w/ and w/o search results in context are generated, incorrect preds are filtered out, the model is trained on those CoT, and then the synthetic CoT is regenerated