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This https://arxiv.org/abs/2403.09680 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Pre-Sorted Tsetlin Machine (The Genetic K-Medoid Method)
This paper proposes a machine learning pre-sort stage to traditional supervised learning using Tsetlin Machines. Initially, K data-points are identified from the dataset using an expedited genetic algorithm to solve the maximum dispersion problem. These are then used as the initial placement to run the K-Medoid clustering algorithm. Finally, an expedited genetic algorithm is used to align K independent Tsetlin Machines by maximising hamming distance. For MNIST level classification problems, res…

Synergizing Privacy and Utility in Data Analytics Through Advanced Information Theorization
Zahir Alsulaimawi
https://arxiv.org/abs/2404.16241 https://

Synergizing Privacy and Utility in Data Analytics Through Advanced Information Theorization
This study develops a novel framework for privacy-preserving data analytics, addressing the critical challenge of balancing data utility with privacy concerns. We introduce three sophisticated algorithms: a Noise-Infusion Technique tailored for high-dimensional image data, a Variational Autoencoder (VAE) for robust feature extraction while masking sensitive attributes and an Expectation Maximization (EM) approach optimized for structured data privacy. Applied to datasets such as Modified MNIST …

Sleep-Like Unsupervised Replay Improves Performance when Data are Limited or Unbalanced
Anthony Bazhenov, Pahan Dewasurendra, Giri Krishnan, Jean Erik Delanois
https://arxiv.org/abs/2402.10956

Sleep-Like Unsupervised Replay Improves Performance when Data are Limited or Unbalanced
The performance of artificial neural networks (ANNs) degrades when training data are limited or imbalanced. In contrast, the human brain can learn quickly from just a few examples. Here, we investigated the role of sleep in improving the performance of ANNs trained with limited data on the MNIST and Fashion MNIST datasets. Sleep was implemented as an unsupervised phase with local Hebbian type learning rules. We found a significant boost in accuracy after the sleep phase for models trained with …

Pre-Sorted Tsetlin Machine (The Genetic K-Medoid Method)
Jordan Morris
https://arxiv.org/abs/2403.09680 https://arxiv.org/pdf/2403.09…

Pre-Sorted Tsetlin Machine (The Genetic K-Medoid Method)
This paper proposes a machine learning pre-sort stage to traditional supervised learning using Tsetlin Machines. Initially, N data-points are identified from the dataset using an expedited genetic algorithm to solve the maximum dispersion problem. These are then used as the initial placement to run the K-Medoid clustering algorithm. Finally, an expedited genetic algorithm is used to align N independent Tsetlin Machines by maximising hamming distance. For MNIST level classification problems, res…