Tabular foundation models excel on tiny- to medium-sized IID data but are outperformed by traditional tree-based and deep learning models on non-IID, large, and high-dimensional datasets, based on evaluations across 11 models and 142 datasets in the new BeyondArena benchmark.
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FlexiFlow optimizes carbon footprint for item-level intelligence on flexible electronics by modeling lifetime variation, delivering 1.62X microarchitectural and 14.5X algorithmic reductions plus a 30.9 kHz tape-out.
Genetic algorithms outperform gradient descent when training DEBI-NN on synthetic and small medical datasets (n=85 to 2126), achieving accuracies of 100% vs 83%, 83% vs 78%, 80% vs 67%, and 81% vs 66%.
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Lifetime-Aware Design for Item-Level Intelligence at the Extreme Edge
FlexiFlow optimizes carbon footprint for item-level intelligence on flexible electronics by modeling lifetime variation, delivering 1.62X microarchitectural and 14.5X algorithmic reductions plus a 30.9 kHz tape-out.