Author

Ali Eslamian

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Preprint Aug 2026

Predicting When Random Low-Dimensional Reparameterizations Train Neural Networks

This work expresses the known accessibility transition in an equivalent conic form, centered for compact convex targets at the statistical dimension of the polar cone, and introduces Random Mapping Networks (RaMaN), which instantiate the predicted latent dimension using structured Hadamard or seed-regenerated Gaussian...

Andrew Cheng, Ali Eslamian, Jie Cheng et al. · 0 citations

#machine learning Preprint Aug 2026

TabNSM: Neural Sparse Mixer for Tabular Regression

TabNSM provides an effective and scalable approach to deep tabular regression, and demonstrates that selective interaction modeling, structured regression supervision, and difficulty-aware sampling provide an effective and scalable approach to deep tabular regression.

Ali Eslamian, Qiang Cheng · 0 citations