Fitting logic gates as 4D multilinear polynomials with covariance Jacobian selection matches or beats 16D softmax baselines on seven datasets and remains stable at 12-layer depth where the baseline drops 37 points on CIFAR-10.
Bishop.Pattern Recognition and Machine Learning
3 Pith papers cite this work. Polarity classification is still indexing.
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A Bayesian finite mixture of cluster-specific low-rank regressions for mixed Gaussian-Bernoulli-negative binomial outcomes, with posterior contraction results and WAIC-based tuning of clusters and rank.
A Dirichlet process mixture model for marked Poisson point processes with squared-link intensities and Laplace variational inference jointly infers clusters, cluster count, and continuous mark-specific intensity surfaces.
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Fitting Multilinear Polynomials for Logic Gate Networks
Fitting logic gates as 4D multilinear polynomials with covariance Jacobian selection matches or beats 16D softmax baselines on seven datasets and remains stable at 12-layer depth where the baseline drops 37 points on CIFAR-10.
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Bayesian low-rank latent-cluster regression for mixed health outcomes
A Bayesian finite mixture of cluster-specific low-rank regressions for mixed Gaussian-Bernoulli-negative binomial outcomes, with posterior contraction results and WAIC-based tuning of clusters and rank.
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Laplace Variational Inference for Dirichlet Process Mixtures of Marked Poisson Point Processes
A Dirichlet process mixture model for marked Poisson point processes with squared-link intensities and Laplace variational inference jointly infers clusters, cluster count, and continuous mark-specific intensity surfaces.