Turtle shell clustering is a new unsupervised probabilistic method that estimates non-linear cluster boundaries, automatically selects the number of components, and handles noise using a regularized mutual information objective with Gaussian-uniform mixtures.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
Simulations show physical neural networks need nonlinearity, amplification, and suppression for learning, with physically plausible circuit designs presented.
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Turtle shell clustering: A mixture approach to discriminative clustering with applications to flow cytometry and other data
Turtle shell clustering is a new unsupervised probabilistic method that estimates non-linear cluster boundaries, automatically selects the number of components, and handles noise using a regularized mutual information objective with Gaussian-uniform mixtures.
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Physical Neural Networks Need Nonlinearity, Amplification, and Suppression for Learning
Simulations show physical neural networks need nonlinearity, amplification, and suppression for learning, with physically plausible circuit designs presented.