PyCUDA
tmu
| PyCUDA | tmu | |
|---|---|---|
| - | 5 | |
| 1,746 | 109 | |
| - | 2.8% | |
| 5.4 | 9.2 | |
| 29 days ago | about 1 month ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | MIT License |
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PyCUDA
We haven't tracked posts mentioning PyCUDA yet.
Tracking mentions began in Dec 2020.
tmu
- Tsetlin machine – the other AI toolbooks
- Tsetlin Machine Unified (TMU) - One Codebase to Rule Them All
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[R] New Tsetlin machine learning scheme creates up to 80x smaller logical rules, benefitting hardware efficiency and interpretability.
Code: https://github.com/cair/tmu
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This Artificial Intelligence (AI) Research From Norway Introduces Tsetlin Machine-Based Autoencoder For Representing Words Using Logical Expressions
Quick Read: https://www.marktechpost.com/2023/01/10/this-artificial-intelligence-ai-research-from-norway-introduces-tsetlin-machine-based-autoencoder-for-representing-words-using-logical-expressions/ Paper: https://arxiv.org/pdf/2301.00709.pdf Github: https://github.com/cair/tmu
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Do we really need 300 floats to represent the meaning of a word? Representing words with words - a logical approach to word embedding using a self-supervised Tsetlin Machine Autoencoder.
Here is a new self-supervised machine learning approach that captures word meaning with concise logical expressions. The logical expressions consist of contextual words like “black,” “cup,” and “hot” to define other words like “coffee,” thus being human-understandable. I raise the question in the heading because our logical embedding performs competitively on several intrinsic and extrinsic benchmarks, matching pre-trained GLoVe embeddings on six downstream classification tasks. Thanks to my clever PhD student Bimal, we now have even more fun and exciting research ahead of us. Our long term research goal is, of course, to provide an energy efficient and transparent alternative to deep learning. You find the paper here: https://arxiv.org/abs/2301.00709 , an implementation of the Tsetlin Machine Autoencoder here: https://github.com/cair/tmu, and a simple word embedding demo here: https://github.com/cair/tmu/blob/main/examples/IMDbAutoEncoderDemo.py.
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