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catch22
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Same prompt, all 22 Turbo Brilliance reasoning modes, one small local model on one GPU: pick your reasoning effort with measured numbers, not vibes
model-selection educational reasoning catch22 llama-cpp local-llm gguf llm-benchmark lfm25 reasoning-modes effort-levels turbo-brilliance davidau
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Oct 31, 2026 - JavaScript
CSCE 5222 Feature Engineering - Group 18 Project 2: Hybrid, Automated, and Interpretable Feature Engineering for Time-Series
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May 29, 2026 - Python
Benchmark testing whether catch22 time-series features can detect genuine chaos even when linear statistics (mean, variance, spectrum) are deliberately matched via IAAFT surrogate decoys.
python time-series random-forest feature-extraction nonlinear-dynamics chaos-theory logistic-map time-series-classification surrogate-data catch22
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Sep 22, 2026 - Python
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