machine-learning-articles
bet
| machine-learning-articles | bet | |
|---|---|---|
| 5 | 3 | |
| 3,143 | 93 | |
| - | - | |
| 4.1 | 2.1 | |
| 2 months ago | 12 months ago | |
| Python | ||
| - | MIT License |
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.
machine-learning-articles
- CNN binary classification validation accuracy reached %77, yet performing poorly on test set?
- Guide to creating a VAE?
-
Minimal PyTorch re-implementation of GPT
For anyone else who was new to the phrase "isotropic model":
https://github.com/christianversloot/machine-learning-articl...
-
CNN Multiclass classification - What happens if in the last layer you use more units than classes?
Of course! I read the documentation and also usage examples and it seems like it should be used with softmax, receiving the class prediction
-
How Vanishing/Exploding Gradient is solved by doing proper Weight Initialization?
Xavier initialization (tanh): https://cs230.stanford.edu/section/4/ He initialization (ReLU): https://github.com/christianversloot/machine-learning-articles/blob/main/he-xavier-initialization-activation-functions-choose-wisely.md
bet
-
Dobb·E: An open-source framework for learning household robotic manipulation
Indeed! In fact, I have a project [0] from last year that uses a GPT-style transformer to address that exact issue :) However, it’s hard to go far outside simulations in real home robotics without a good platform, out of which efforts came Dobb-E.
[0] https://mahis.life/bet/
- Minimal PyTorch re-implementation of GPT
-
Show HN: We trained a (mini) GPT for multi-modal robot behaviors
Hi HN!
First author of the paper here, thought some of you may enjoy reading about this! Even now, training robots on human demonstration data is the best way to get them to do new and exciting things in the real world. However, this generally requires a lot of data curation in the standard way: the robots can only follow along if you give them data that is solving a single task in a single way.
To improve the status quo, we introduce Behavior Transformer in this paper, which can learn from unlabeled demonstration data solving multiple different tasks in different ways using a GPT-like generator model. We had to make some modifications to fit the continuous actions, unlike the standard GPT model which fits discrete words.
As it turns out, unconditional rollouts from this model shows a lot more "natural" behavior (i.e. different tasks solved in different rollouts in different ways)_than standard behavioral cloning. More importantly, behavior transformers show much better mode coverage compared to previous models, and show some level of compositionality. Check out our videos! [1]
Finally, another oft-ignored part I am quite proud of is our code release -- we worked quite hard to make sure our code [2] is easy to read, reproduce, and remix! And also, did I tell you that these models train super fast? The Franka Kitchen environment in the top video [3] takes just 10 minutes on an Nvidia 3080 to the point you are seeing in the video. Compare that with standard RL training, and you might agree with me that a small number of demonstrations can truly go a long way!
Happy to answer questions, as well! Have a great Friday, wherever you are :)
[1] https://mahis.life/bet
[2] https://github.com/notmahi/bet
[3] https://mahis.life/bet/more/kitchen/
What are some alternatives?
iris - Transformers are Sample-Efficient World Models. ICLR 2023, notable top 5%.
minGPT - A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
embedding-encoder - Scikit-Learn compatible transformer that turns categorical variables into dense entity embeddings.
awesome-adaptive-computation - A curated reading list of research in Adaptive Computation, Dynamic Compute & Mixture of Experts (MoE).