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Gradient boosting framework speeds up tensor network learning

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new gradient boosting framework called GB-CPKM to improve the efficiency of Tensor Network Kernel Machines (TNKMs). This method sequentially adds learners to increase model capacity, offering a more efficient approach to rank selection compared to traditional cross-validation methods. Experiments demonstrate that GB-CPKM can train up to six times faster and use half the peak memory of conventional techniques while maintaining comparable predictive performance. AI

IMPACT This research offers a more computationally efficient method for training complex machine learning models, potentially accelerating research and development in areas utilizing tensor network kernel machines.

RANK_REASON The cluster contains a research paper detailing a new machine learning method. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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Gradient boosting framework speeds up tensor network learning

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The cluster contains a research paper detailing a new machine learning method. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Albert Saiapin, Kim Batselier ·

    Gradient Boosting for Efficient Rank Discovery in Tensor Network Kernel Machines

    arXiv:2611.08093v1 Announce Type: cross Abstract: Tensor network kernel machines (TNKMs) combine nonlinear features with low-rank tensor representations to model complex relationships efficiently. However, selecting an appropriate tensor rank remains challenging, as conventional …

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