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Singleshot : a scalable Tucker tensor decomposition
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use Singleshot instead of in-memory baselines (GreedyHOOI, Tensorsketch, Scalrandomtucker) at large tensor size | Tucker decomposition · Movielens and Enron, large M | ability to run (memory) | Helped |
| use Singleshot instead of baselines at small tensor size | Tucker decomposition · Movielens MAP, Enron AE | MAP / approximation error | No clear effect |
| use Singleshot instead of Scalrandomtucker | Tucker decomposition · Movielens and Enron | running time | Hurt |
| use inexact gradient from a subset of subtensors (Singleshotinexact vs Singleshot) (ablation) | Singleshot · Movielens and Enron | running time | Helped |
| use Singleshot positive instead of posScalrandomtucker (non-negative) | non-negative Tucker decomposition · Movielens and Enron | approximation error | Helped |
| use Singleshotonline positive instead of Tensorsketchonline | online Tucker decomposition · Enron 20000x2000x50 | running time | Helped |
Closest papers by meaning
- Low-Rank Tucker Decomposition of Large Tensors Using TensorSketch (NeurIPS 2018 · similarity 0.90)
- Tensor Decomposition with Smoothness (ICML 2017 · similarity 0.89)
- Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data (ICLR 2024 · similarity 0.88)
- Geometry-aware training of factorized layers in tensor Tucker format (NeurIPS 2024 · similarity 0.88)
- Fast Tensor Completion via Approximate Richardson Iteration (ICML 2025 · similarity 0.87)
- Efficient Leverage Score Sampling for Tensor Train Decomposition (NeurIPS 2024 · similarity 0.86)
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