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Research map / Learning theory and algorithms / Matrix and tensor methods

Tensor network decompositions

Represents high-order tensors with tensor networks and tensor trains, with theory on their learning capacity.

45 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026) · an approach in Matrix and tensor methods, keyword cluster “tensors · decomposition · rank tensor”

State History Tried Preprints Who Export

Where it stands

Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.

4 papers in 2023–24 and 11 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic grew, from 2.3% to 5.9%.

How this is decided

A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.

How it got here

2016: 3162017: 5172018: 2182019: 3192020: 6202021: 5212022: 6222023: 2232024: 2242025: 6252026: 526

Most cited paper of each year

Earliest papers here

What has been tried

37 papers here report 276 measured changes (a component added, removed or swapped, compared with a baseline): helped 63%, no clear effect 14%, hurt 15%, mixed 8%. Papers mostly publish changes that helped, so "helped" is over-represented.

Ablations in recent papers

What was changedMeasured onResultEffectPaper
Increase hypervector dimensionFactorization · peak probability of finding a marked stateHelpedHDQMF: Holographic Feature Decomposition Using Quantum Algorithms (CVPR 2024 · 4 citations)
Add more channels to GTP3D Tetris pieces · ability to distinguish chiral piecesNo clear effectThe Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products (ICML 2025 · 0 citations)
Increase the number of source tasks from K=1 to K=8akiyo video completion · PSNR22.19 -> 40.32HelpedLow-Rank Tensor Transitions (LoRT) for Transferable Tensor Regression (ICML 2025)

Recent preprints

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    Who publishes on it

    Institutions on the most papers of this approach since 2023 (of 15 papers; from OpenAlex and the papers' affiliations).

    Use it

    Download BibTeX of 32 key papersOpen the topic in the interactive map

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    Same keyword cluster

    Elsewhere in the topic

    Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).

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