AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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The gradient flow of the best rank-$r$ approximation is derived and it is pointed out that the offset of DLRT comes from a curvature-coupling term which is large and thus non-negligible under aggressive compression, and a stable dynamic low-rank training method is proposed, named SDLRT.
Experiments on five real-world datasets show that \textsc{DS-Frame} consistently improves representative sequential recommendation backbones, with larger gains on challenging groups and effective accuracy--efficiency trade-offs, highlighting the potential of adaptive inference for more efficient and robust recommendati...
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