CRONOS introduces scalable convex optimization for two-layer neural networks reaching ImageNet scale, with CRONOS-AM extending to arbitrary multi-layer architectures while matching tuned deep learning performance.
Scaling laws for deep learning
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
Bounded performance metrics always favor convergence of AI capabilities to meek models while unbounded metrics allow frontier models to maintain leads indefinitely, with policy implications for capability concentration.
citing papers explorer
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CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks
CRONOS introduces scalable convex optimization for two-layer neural networks reaching ImageNet scale, with CRONOS-AM extending to arbitrary multi-layer architectures while matching tuned deep learning performance.
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Two AI Metrics Diverged: Will it Make All the Difference?
Bounded performance metrics always favor convergence of AI capabilities to meek models while unbounded metrics allow frontier models to maintain leads indefinitely, with policy implications for capability concentration.