GODM
TsetlinMachine
| GODM | TsetlinMachine | |
|---|---|---|
| 1 | 3 | |
| 53 | 451 | |
| - | 2.4% | |
| 0.0 | 3.4 | |
| over 2 years ago | 20 days ago | |
| Jupyter Notebook | Cython | |
| - | MIT License |
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GODM
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Study reveals that animals cope with environmental complexity by reducing the world into a series of sequential two-choice decisions and use an algorithm to make a decision, a strategy that results in highly effective decision-making no matter how many options there are
Wow, this is so unbelievably cool! And they even made their data and code available! https://github.com/vivekhsridhar/GODM
TsetlinMachine
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[D] Are there unconventional cognition architectures that learn without SGD, weights between neurons, or can only be done on the CPU?
But for unconventional / back to the roots Tsetlin machines is a candidate, https://github.com/cair/TsetlinMachine
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Study reveals that animals cope with environmental complexity by reducing the world into a series of sequential two-choice decisions and use an algorithm to make a decision, a strategy that results in highly effective decision-making no matter how many options there are
Reminds me of Tsetlin machines
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[R] Drop Clause boosts Tsetlin Machine accuracy up to +4% and training speed up to 4x
Hi u/pddpro, there are unfortunately few tutorials available. There are some video resources here: https://github.com/cair/TsetlinMachine#videos and some demos here: https://github.com/cair/pyTsetlinMachine. Currently writing a book: Introduction to Tsetlin Machines. Plan to share drafts of the chapters here as I proceed, including tutorials.
What are some alternatives?
fim - FIM is an Open Source Host-based file integrity monitoring tool that performs file system analysis, file integrity checking, real time alerting and provides Audit daemon data.
pyTsetlinMachine - Implements the Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, Weighted Tsetlin Machine, and Embedding Tsetlin Machine, with support for continuous features, multigranularity, clause indexing, and literal budget
deep-learning-drizzle - Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!
tmu - Implements the Tsetlin Machine, Coalesced Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features, drop clause, Type III Feedback, focused negative sampling, multi-task classifier, autoencoder, literal budget, and one-vs-one multi-class classifier. TMU is written in Python with wrappers for C and CUDA-based clause evaluation and updating.
ta-lib-python - Python wrapper for TA-Lib (http://ta-lib.org/).