Presents an incremental TT compression algorithm operating directly on streaming TT-formatted data via core-wise operations, with error bounds and application to ROM construction from low-rank snapshots.
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2 Pith papers cite this work, alongside 10 external citations. Polarity classification is still indexing.
2
Pith papers citing it
10
external citations · OpenAlex
years
2026 2verdicts
UNVERDICTED 2representative citing papers
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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Incremental Tensor-Train Compression from Streaming TT-Formatted Data: Applications to Reduced-Order Modeling
Presents an incremental TT compression algorithm operating directly on streaming TT-formatted data via core-wise operations, with error bounds and application to ROM construction from low-rank snapshots.
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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.