Projects in Awesome Lists by cair
A curated list of projects in awesome lists by cair .
https://github.com/cair/tsetlinmachine
Code and datasets for the Tsetlin Machine
bandit-learning frequent-pattern-mining game-theory learning-automata machine-learning pattern-recognition propositional-logic tsetlin-machine
Last synced: 29 Mar 2025
https://github.com/cair/fire-detection-image-dataset
This dataset contains normal images and images with fire. It is highly unbalanced to reciprocate real world situations. It consists of a variety of scenarios and different fire situations (intensity, luminosity, size, environment etc).
Last synced: 29 Mar 2025
https://github.com/cair/deep-rts
A Real-Time-Strategy game for Deep Learning research
ai artificial-intelligence cpp deep-learning deep-reinforcement-learning game machine-learning neural-networks per-arne python reinforcement-learning tree-search
Last synced: 19 Dec 2024
https://github.com/cair/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
bandit-learning classification convolution embedding frequent-pattern-mining interpretable machine-learning propositional-logic regression rule-based tsetlin-machine
Last synced: 09 Apr 2025
https://github.com/cair/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
bandit-learning classification convolution embedding frequent-pattern-mining interpretable machine-learning propositional-logic regression rule-based tsetlin-machine
Last synced: 13 Nov 2024
https://github.com/cair/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.
absorbing-states autoencoder convolution cuda gpu incremental incremental-computation multi-output pattern-recognition propositional-logic regression relational-logic sparse tsetlin-machine
Last synced: 09 Apr 2025
https://github.com/cair/fast-tsetlin-machine-with-mnist-demo
A fast Tsetlin Machine implementation employing bit-wise operators, with MNIST demo.
artificial-intelligence bitwise-operators explainable-ai frequent-pattern-mining machine-learning mnist pattern-recognition rule-based tsetlin-machine
Last synced: 13 Apr 2025
https://github.com/cair/convolutional-tsetlin-machine-tutorial
Tutorial on the Convolutional Tsetlin Machine
bandit-learning convolution frequent-pattern-mining interpretable-machine-learning pattern-recognition propositional-logic rule-based tsetlin-machine
Last synced: 13 Apr 2025
https://github.com/cair/TextUnderstandingTsetlinMachine
Using the Tsetlin Machine to learn human-interpretable rules for high-accuracy text categorization with medical applications
deep-learning frequent-pattern-mining medical medical-informatics text-classification tsetlin-machine
Last synced: 15 Mar 2025
https://github.com/cair/textunderstandingtsetlinmachine
Using the Tsetlin Machine to learn human-interpretable rules for high-accuracy text categorization with medical applications
deep-learning frequent-pattern-mining medical medical-informatics text-classification tsetlin-machine
Last synced: 13 Apr 2025
https://github.com/cair/tsetlinmachinebook
Python code accompanying the book "An Introduction to Tsetlin Machines".
Last synced: 13 Apr 2025
https://github.com/cair/pytsetlinmachineparallel
Multi-threaded implementation of the Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features and multigranularity.
bandit-learning classification convolution frequent-pattern-mining interpretable-machine-learning machine-learning propositional-logic regression rule-based tsetlin-machine
Last synced: 13 Apr 2025
https://github.com/cair/pytsetlinmachinecuda
Massively Parallel and Asynchronous Architecture for Logic-based AI
classification convolution cuda gpu learning-automata logic-based-artificial-intelligence regression tsetlin-machine
Last synced: 13 Apr 2025
https://github.com/cair/fast-tsetlin-machine-in-cuda-with-imdb-demo
A CUDA implementation of the Tsetlin Machine based on bitwise operators
bitwise-operators cuda explainable-artificial-intelligence gpu-computing pattern-recognition tsetlin-machine
Last synced: 13 Apr 2025
https://github.com/cair/open-tsetlin-machine
Open Source Tsetlin Machine framework
machine-learning tsetlin-machine
Last synced: 29 Mar 2025
https://github.com/cair/tsetlinmachinec
A C implementation of the Tsetlin Machine
Last synced: 13 Apr 2025
https://github.com/cair/regression-tsetlin-machine
Implementation of the Regression Tsetlin Machine
machine-learning regression tsetlin-machine
Last synced: 13 Apr 2025
https://github.com/cair/deep-warehouse
A Simulator for complex logistic environments
ai artificial-intelligence cpp deep-learning deep-reinforcement-learning game machine-learning neural-networks per-arne python reinforcement-learning reinforcement-learning-environments tree-search
Last synced: 13 Apr 2025
https://github.com/cair/axis_and_allies
A simple Axis & Allies engine.
artificial-intelligence neural-network risk-game
Last synced: 13 Apr 2025
https://github.com/cair/tm-xor-proof
#tsetlin-machine #machine-learning #game-theory #propositional-logic #pattern-recognition #bandit-learning #frequent-pattern-mining #learning-automata
Last synced: 29 Mar 2025
https://github.com/cair/tmu-datasets
A dataset repository for datasets in tmu
Last synced: 29 Mar 2025
https://github.com/cair/python-fast-tsetlin-machine
Python wrapper for https://github.com/cair/fast-tsetlin-machine-with-mnist-demo
Last synced: 29 Mar 2025
https://github.com/cair/icml-massively-parallel-and-asynchronous-tsetlin-machine-architecture
Code repository for ICML 21 for Paper titled Massively Parallel and Asynchronous Tsetlin Machine Architecture
Last synced: 13 Apr 2025
https://github.com/cair/corrembed_evaluating_pre-trained_model_efficacy
Code for the paper "CorrEmbed: Evaluating Pre-trained Model Efficacy with a Novel Metric Integrating Image Embedding and Tag Correlation"
Last synced: 29 Mar 2025
https://github.com/cair/deterministic-tsetlin-machine
Due to the high energy consumption and scalability challenges of deep learning, there is a critical need to shift research focus towards dealing with energy consumption constraints. Tsetlin Machines (TMs) are a recent approach to machine learning that has demonstrated significantly reduced energy usage compared to neural networks alike, while performing competitively accuracy-wise on several benchmarks. However, TMs rely heavily on energy-costly random number generation to stochastically guide a team of Tsetlin Automata to a Nash Equilibrium of the TM game. In this paper, we propose a novel finite-state learning automaton that can replace the Tsetlin Automata in TM learning, for increased determinism. The new automaton uses multi-step deterministic state jumps to reinforce sub-patterns. Simultaneously, flipping a coin to skip every d'th state update ensures diversification by randomization. The d-parameter thus allows the degree of randomization to be finely controlled. E.g., d=1 makes every update random and d=infinity makes the automaton completely deterministic. Our empirical results show that, overall, only substantial degrees of determinism reduces accuracy. Energy-wise, random number generation constitutes switching energy consumption of the TM, saving up to 11 mW power for larger datasets with high d values. We can thus use the new d-parameter to trade off accuracy against energy consumption, to facilitate low-energy machine learning.
Last synced: 29 Mar 2025
https://github.com/cair/deepaxie
Implementation of a simplified Axie Infinity Environment in C++ that is used to train an agent with the reinforcement learning algorithm DQN to play the game.
Last synced: 29 Mar 2025
https://github.com/cair/py_image_stitcher
A small library for stitching together images, from Numpy or PIL Sources
Last synced: 29 Mar 2025
https://github.com/cair/tsetlin-machine-deep-neural-network-recommendation-system-comparison
Last synced: 29 Mar 2025
https://github.com/cair/ray-bugfix
A workaround to issues with Rllib, given it does not work for your current gym environment. CarRacing-v0 is one of these.
Last synced: 29 Mar 2025
https://github.com/cair/patchformer
PatchFormer - Improved dense predictions using implicit representation learning
Last synced: 29 Mar 2025
https://github.com/cair/hex-ai
Various AIs for the board game hex, including Monte Carlo Tree Search with the Tsetlin Machine
ai hex machine-learning monte-carlo-tree-search tsetlin-machine
Last synced: 29 Mar 2025
https://github.com/cair/an-optimized-toolbox-for-advanced-image-processing-with-tsetlin-machine-composites
An Optimized Toolbox for Advanced Image Processing with Tsetlin Machine Composites
Last synced: 29 Mar 2025
https://github.com/cair/welcome-to-cair
Introduction and outline of CAIR Research
Last synced: 29 Mar 2025
https://github.com/cair/pycoalescedtsetlinmachinecuda
PyCUDA implementation of the Coalesced Multi-Output Tsetlin Machine
logic-based-artificial-intelligence machine-learning rule-based tsetlin-machine
Last synced: 13 Apr 2025