8 papers
Deterministic Sparse FFT via Keyed Multi-View Gating with Expected Time
Aaron R. Flouro, Shawn P. Chadwick
We introduce a deterministic sparse Fourier transform framework based on a keyed multi-view gating mechanism that leverages 2-of-3 Chinese Remainder Theorem (CRT) agreement to redu…
Safety-Certified CRT Sparse FFT: Lower Bound and Worst-Case
Aaron R. Flouro, Shawn P. Chadwick
Computing Fourier transforms of k-sparse signals, where only k of N frequencies are non-zero, is fundamental in compressed sensing, radar, and medical imaging. While the Fast Fouri…
Post-Training Probability Manifold Correction via Structured SVD Pruning and Self-Referential Distillation
Aaron R. Flouro, Shawn P. Chadwick
Large language models are expensive to deploy. We introduce Sparse Knowledge Distillation (SparseKD), a post-training method that compresses transformer models by combining structu…
Adaptive Weighting in Knowledge Distillation: An Axiomatic Framework for Multi-Scale Teacher Ensemble Optimization
Aaron R. Flouro, Shawn P. Chadwick
Knowledge distillation with multiple teachers is increasingly used to improve robustness, efficiency, and safety, yet existing approaches rely largely on heuristic or implementatio…
Recursive Meta-Distillation: An Axiomatic Framework for Iterative Knowledge Refinement
Aaron R. Flouro, Shawn P. Chadwick
Recent work in probability-domain knowledge distillation has established axiomatic frameworks for temperature scaling, multi-teacher aggregation, and bias-variance trade-offs in si…
Multi-Teacher Ensemble Distillation: A Mathematical Framework for Probability-Domain Knowledge Aggregation
Aaron R. Flouro, Shawn P. Chadwick
Building on the probability-domain distillation framework of Sparse-KD, we develop an axiomatic, operator-theoretic framework for multi-teacher ensemble knowledge distillation. Rat…