Research map / Probabilistic and scientific modeling / Variational and generative sampling
Distributed Flexible Nonlinear Tensor Factorization
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use balanced training entries containing zeros and nonzeros | Flexible GP tensor factorization · Alog, AdClick, Enron, NellSmall | predictive performance | Helped |
| Use balanced training entries rather than unbalanced entries | CP · Alog, AdClick, Enron, NellSmall | predictive performance | Helped |
| Replace Kronecker-structured InfTucker with flexible GP tensor factorization | InfTucker · Small tensor datasets | prediction accuracy | Helped |
| Use flexible GP tensor factorization instead of InfTuckerEx | InfTuckerEx · Alog, AdClick | prediction accuracy | Helped |
| Use flexible GP tensor factorization instead of InfTuckerEx | InfTuckerEx · Enron, NellSmall | prediction accuracy | Mixed |
| Use nonlinear tensor factorization instead of GigaTensor | GigaTensor · ACC, DBLP, NELL | predictive performance | Helped |
| Use flexible GP tensor factorization instead of InfTuckerEx | InfTuckerEx · ACC, DBLP | predictive performance | Helped |
| Use flexible GP tensor factorization instead of InfTuckerEx | InfTuckerEx · NELL | predictive performance | Hurt |
| Use key-value-free MapReduce rather than traditional key-value processing | Distributed inference · Tensor size 100 × 100 × 100 | speed acceleration: 30 times | Helped |
| Use flexible GP tensor factorization instead of logistic regression | CTR prediction · Online ads click log | AUC: 20.7% | Helped |
| Use flexible GP tensor factorization instead of linear SVM | CTR prediction · Online ads click log | AUC: 20.8% | Helped |
| Use a tight ELBO that subsumes optimal variational posteriors rather than sequential E-M updates | Distributed variational inference · Continuous and binary tensor inference | inference efficiency | Helped |
Closest papers by meaning
- SWIFT: Scalable Wasserstein Factorization for Sparse Nonnegative Tensors (AAAI 2021 · similarity 0.89)
- Geometry-aware training of factorized layers in tensor Tucker format (NeurIPS 2024 · similarity 0.87)
- Performance Gaps in Multi-view Clustering under the Nested Matrix-Tensor Model (ICLR 2024 · similarity 0.86)
- High-Dimensional Tensor Regression With Oracle Properties (ICML 2025 · similarity 0.85)
- Deep Rank-One Tensor Functional Factorization for Multi-Dimensional Data Recovery (AAAI 2025 · similarity 0.85)
- Efficient Nonparametric Tensor Decomposition for Binary and Count Data (AAAI 2024 · similarity 0.85)
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