arXiv:2606.18676v1 Announce Type: new Abstract: Training-free neural architecture search promises efficient discovery of high-performance networks without costly training. However, existing zero-cost proxies rely on fragmented heuristics that fail to capture the fundamental question: what makes an architecture trainable? This paper introduces Intrinsic Trainability (InTrain), a unified theoretical proxy that formalizes trainability as an architectural invariant emerging from two synergistic components: geometric capacity and optimization resilience. We operationalize intrinsic trainability thr
The proliferation of complex AI models necessitates more efficient architecture search methods to reduce computational costs and accelerate development, especially as AI applications scale.
Zero-cost neural architecture search is critical for democratizing AI development and enabling faster iteration cycles, particularly for resource-constrained environments or novel applications.
The introduction of Intrinsic Trainability (InTrain) provides a more robust and theoretically grounded proxy for identifying high-performance AI architectures without expensive training, potentially speeding up model design significantly.
- · AI researchers
- · Small AI companies
- · Edge AI developers
- · Deep learning frameworks
- · Companies reliant on brute-force NAS
- · Inefficient AI development pipelines
Reduced computational resources required for developing new AI models.
Faster innovation cycles in AI research and more rapid deployment of specialized AI solutions.
Lower barriers to entry for AI development, potentially leading to a wider array of AI applications and increased competition.
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Read at arXiv cs.LG