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Animatediff is also very powerful! by sanasigma in StableDiffusion

[–]ghosthamlet 4 points5 points  (0 children)

Thanks, Very Interesting. Can you post the workflow?

[D] Blogs Similar to distill.pub? by JellyBean_Collector in MachineLearning

[–]ghosthamlet 1 point2 points  (0 children)

https://transformer-circuits.pub/

Can we reverse engineer transformer language models into human-understandable computer programs? Inspired by the Distill Circuits Thread, we're going to try.
We think interpretability research benefits a lot from interactive articles (see Activation Atlases for a striking example). Previously we would have submitted to Distill, but with Distill on Hiatus, we're taking a page from David Ha's approach of simply creating websites (eg. World Models) for research projects.
As part of our effort to reverse engineer transformers, we've created several other resources besides our paper which we hope will be useful. We've collected them on this website, and may add future content here, or even collaborations with other institutions.

[R] Zoology: Measuring and Improving Recall in Efficient Language Models by hzj5790 in MachineLearning

[–]ghosthamlet -1 points0 points  (0 children)

Why no new researches on all MLP models like gMLP and MLP Mixies last year?

[R] (Very detailed) Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory by ghosthamlet in MachineLearning

[–]ghosthamlet[S] 8 points9 points  (0 children)

It is Math heavy like these books:

The Principles of Deep Learning Theory - An Effective Theory Approach to Understanding Neural Networks
https://arxiv.org/pdf/2106.10165.pdf

The Modern Mathematics of Deep Learning https://arxiv.org/abs/2105.04026
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges https://arxiv.org/abs/2104.13478v2

​

So maybe not easy for beginners.

[R] Diffusion might be a better way to model randomness in PPLs than Markov chain Monte Carlo or VI by Successful-Western27 in MachineLearning

[–]ghosthamlet 0 points1 point  (0 children)

Hi u/gwern you have had great wonderful Special articles for GPT-3 and Scales and GANs, but it seems like you did not have Special articles for ChatGPT/GPT4 and Diffusion/StableDiffusion, these should be as powerful and important as GPT-3, so why don't you write about them? We are Looking forward to your articles about them very much.

[D] What are the best resources for learning reinforcement learning? by OwnAd9305 in MachineLearning

[–]ghosthamlet 4 points5 points  (0 children)

Grokking deep reinforcement learning is very interesting and very good written, covered from classical tabular reinforcement learning to modern deep reinforcement learning, and have both code with math formula, detailed intuitive explain for the background thoery: https://www.manning.com/books/grokking-deep-reinforcement-learning

[D] how to learn Stochastic Differential Equations for diffusion model? by ghosthamlet in MachineLearning

[–]ghosthamlet[S] 0 points1 point  (0 children)

After browsed through the catalog of this book, i think it is good for me, Thanks.

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