DGPO: RL-Steered Graph Diffusion for Neural Architecture Generation

Ali NematiFeb 2426 sec read11 views

Researchers introduced Directed Graph Policy Optimization (DGPO), which uses reinforcement learning to fine-tune graph diffusion models specifically for directed acyclic graphs used in neural architecture search. This advancement allows for more efficient and effective generation of near-optimal neural architectures with minimal training data, demonstrating the model's capability to learn transferable structural priors and achieve performance close to or surpassing benchmarks.

Read the full article at arXiv cs.LG (ML)

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