Research map / Probabilistic and scientific modeling / Molecules and proteins
Probabilistic Boolean Tensor Decomposition
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Probabilistic (Bayesian sampling) Boolean tensor decomposition instead of dbtf | random 20x20x20 tensors · simulated random tensors | reconstruction accuracy: distinctly higher accuracies throughout all conditions | Helped |
| Use posterior averaging for reconstruction instead of MAP factors (ablation) | TensOrMachine reconstruction · simulated tensors, noise levels | reconstruction accuracy: virtually no impact below 30% noise | No clear effect |
| Bayesian Occam's razor or cross validation instead of MDL for choosing latent dimensionality (ablation) | Boolean tensor rank selection · simulated random matrices | relative error in predicting tensor rank: both approaches clearly outperform MDL | Helped |
| Cross validation instead of Bayesian Occam's razor for rank selection (ablation) | Boolean tensor rank selection · moderate noise simulated data | relative error in predicting tensor rank | Helped |
| Relational encoding of continuous gene expression as binary tensor (TensOrM) instead of PCA features | TCGA cancer type classification · TCGA patient representations, random forest | predictive accuracy: approx. 91% vs 86% for PCA | Helped |
| Probabilistic TensOrMachine instead of dbtf features | TCGA cancer type classification · TCGA patient representations, random forest | predictive accuracy: approx. 91% vs 81% for dbtf | Helped |
Closest papers by meaning
- Bayesian Boolean Matrix Factorisation (ICML 2017 · similarity 0.90)
- Undirected Probabilistic Model for Tensor Decomposition (NeurIPS 2023 · similarity 0.87)
- Geometric All-way Boolean Tensor Decomposition (NeurIPS 2020 · similarity 0.87)
- Efficient Nonparametric Tensor Decomposition for Binary and Count Data (AAAI 2024 · similarity 0.84)
- Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data (ICLR 2024 · similarity 0.84)
- Tensor Decomposition Meets Knowledge Compilation: A Study Comparing Tensor Trains with OBDDs (AAAI 2025 · similarity 0.84)
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