Name: Centre for Artificial Intelligence Research (CAIR)
Type: Organization
Bio: CAIR is a centre for research excellence on artificial intelligence at the University of Agder. We attack unsolved problems, seeking superintelligence.
Location: Grimstad, Norway
Blog: https://cair.uia.no/
Centre for Artificial Intelligence Research (CAIR)'s Projects
🐤 Nix-TTS: An Incredibly Lightweight End-to-End Text-to-Speech Model via Non End-to-End Distillation
A collection of jupyter notebooks
Open Source Tsetlin Machine framework
PatchFormer - Improved dense predictions using implicit representation learning
Plug-and-play collaboration between specialized Tsetlin machines
A small library for stitching together images, from Numpy or PIL Sources
PyCUDA implementation of the Coalesced Multi-Output Tsetlin Machine
PyCUDA implementation of the Coalesced Multi-Output Tsetlin Machine
PyCUDA implementation of the Coalesced Multi-Output Tsetlin Machine
Python wrapper for https://github.com/cair/fast-tsetlin-machine-with-mnist-demo
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
Massively Parallel and Asynchronous Architecture for Logic-based AI
Multi-threaded implementation of the Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features and multigranularity.
VNC Client Library for Python
A workaround to issues with Rllib, given it does not work for your current gym environment. CarRacing-v0 is one of these.
Implementation of the Regression Tsetlin Machine
stm base project
Using the Tsetlin Machine to learn human-interpretable rules for high-accuracy text categorization with medical applications
#tsetlin-machine #machine-learning #game-theory #propositional-logic #pattern-recognition #bandit-learning #frequent-pattern-mining #learning-automata
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.
A dataset repository for datasets in tmu