SWAP-Score evaluates neural networks without training by quantifying sample-wise activation patterns, achieving high correlation with true performance on CIFAR-10 for CNNs and GLUE for Transformers while enabling fast NAS.
Nas-bench-301 and the case for surrogate benchmarks for neural architecture search
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Experimental comparison of 15 HPO and NAS algorithms for automated feature preprocessing on 45 tabular datasets finds evolution-based methods and random search as top performers.
EZR.py shows that a compact, readable Python toolkit can match or exceed state-of-the-art tools like SHAP, LIME, SMAC3, and FASTREAD on over 120 tabular SE tasks while running 500 times faster and using far less labeled data.
Reviews NAS methods through bilevel optimization lens, categorizing them into sampling-based and theory-based, and proposes an auxiliary math programming framework for more principled architecture and weight updates.
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Zero-Shot Neural Network Evaluation with Sample-Wise Activation Patterns
SWAP-Score evaluates neural networks without training by quantifying sample-wise activation patterns, achieving high correlation with true performance on CIFAR-10 for CNNs and GLUE for Transformers while enabling fast NAS.
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Can AI be Easy? Lessons Learned from the EZR.py Toolkit
EZR.py shows that a compact, readable Python toolkit can match or exceed state-of-the-art tools like SHAP, LIME, SMAC3, and FASTREAD on over 120 tabular SE tasks while running 500 times faster and using far less labeled data.
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Bilevel Optimization for Neural Architecture Search
Reviews NAS methods through bilevel optimization lens, categorizing them into sampling-based and theory-based, and proposes an auxiliary math programming framework for more principled architecture and weight updates.