block-recurrent-transformer-pytorch
Implementation of Block Recurrent Transformer - Pytorch (by lucidrains)
iris
Transformers are Sample-Efficient World Models. ICLR 2023, notable top 5%. (by eloialonso)
block-recurrent-transformer-pytorch | iris | |
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1 | 8 | |
204 | 756 | |
- | - | |
5.0 | 1.9 | |
10 months ago | 2 months ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 only |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
block-recurrent-transformer-pytorch
Posts with mentions or reviews of block-recurrent-transformer-pytorch.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-09.
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From Deep to Long Learning
that line of research is still going. https://github.com/lucidrains/block-recurrent-transformer-py... i think it is worth continuing research on both fronts.
iris
Posts with mentions or reviews of iris.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-09.
-
From Deep to Long Learning
Yea, after all these LLMs are predicting one sequence of tokens from another sequence of tokens and the tokens could be anything, it just "happens" that text has the most knowledge and the easiest to input, then there are image, sound, video, but tokens could also be learned from world experience in RL:
Transformers are Sample-Efficient World Models:
https://github.com/eloialonso/iris#transformers-are-sample-e...
- What is the next booming topic in Deep RL?
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Most Popular AI Research Sept 2022 - Ranked Based On Total GitHub Stars
Transformers are Sample Efficient World Models https://github.com/eloialonso/iris https://arxiv.org/abs/2209.00588v1
- [D] Most Popular AI Research Sept 2022 - Ranked Based On GitHub Stars
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Minimal PyTorch re-implementation of GPT
This is actually a pretty neat, self-contained implementation that can super easily extended beyond stereotypical natural language models, for example to create world models for video games [1] or to create robot models that can learn to imitate from large, chaotic human demonstration data [2] (disclaimer, I'm an author on the second one.) Basically, GPT (or minGPT) models are EXCELLENT sequence modelers, almost to the point where you can throw any sensible sequence data at it and hope to get interesting results, as long as you don't overfit.
Even though I have only been working on machine learning for around six years, it's crazy to see how the landscape has changed so fast so recently, including diffusion models and transformers. It's not too much to say that we might expect more major breakthroughs by the end of this decade, and end in a place we can't even imagine right now!
[1] https://github.com/eloialonso/iris
- Transformers are Sample Efficient World Models
- [R] Transformers are Sample Efficient World Models: With the equivalent of only two hours of gameplay in the Atari 100k benchmark, IRIS outperforms humans on 10 out of 26 games and surpasses MuZero.
What are some alternatives?
When comparing block-recurrent-transformer-pytorch and iris you can also consider the following projects:
flash-attention-jax - Implementation of Flash Attention in Jax
setfit - Efficient few-shot learning with Sentence Transformers