Tensor decompositions face practical limits in large-scale LLM compression due to mismatch between assumed shared subspaces and heterogeneous model representations.
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Tensor networks developed for quantum states are reviewed as tools for machine learning models, with assessment of their potential computational, explanatory, and privacy advantages alongside remaining challenges.
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Rethinking the Role of Tensor Decompositions in Post-Training LLM Compression
Tensor decompositions face practical limits in large-scale LLM compression due to mismatch between assumed shared subspaces and heterogeneous model representations.
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Quantum-inspired tensor networks in machine learning models
Tensor networks developed for quantum states are reviewed as tools for machine learning models, with assessment of their potential computational, explanatory, and privacy advantages alongside remaining challenges.