Counterexamples to the unimodal minimal filling architecture conjecture for PNNs, discovered via frontier search, dimension bounds on neurovarieties, and symbolic computation; some subarchitectures show large defect.
Polynomial activation neural networks: Modeling, stability analysis and coverage bp-training
2 Pith papers cite this work, alongside 17 external citations. Polarity classification is still indexing.
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Power-Softmax is a new HE-compatible attention variant that permits training and inference of billion-parameter polynomial LLMs with performance matching standard transformers.
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Minimal Filling Architectures of Polynomial Neural Networks: Counterexamples, Frontier Search, and Defects
Counterexamples to the unimodal minimal filling architecture conjecture for PNNs, discovered via frontier search, dimension bounds on neurovarieties, and symbolic computation; some subarchitectures show large defect.
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Power-Softmax: Towards Secure LLM Inference over Encrypted Data
Power-Softmax is a new HE-compatible attention variant that permits training and inference of billion-parameter polynomial LLMs with performance matching standard transformers.