Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Streaming Bayesian Deep Tensor Factorization
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace SVB with moment-matching ADF/EP streaming inference for the deep tensor model (SBDT vs SVB-DTF) | Bayesian deep tensor factorization · DBLP, Anime, ACC, MovieLen1M | AUC/RMSE | Helped |
| Add deep NN (SBDT) instead of multilinear CP form (POST) | streaming tensor factorization · all four datasets | AUC/RMSE | Helped |
| Use SBDT instead of POST for running time | streaming factorization · DBLP, ACC, Anime, MovieLen1M | running time | Mixed |
| Use ReLU rather than tanh activation in SBDT (ablation) | SBDT · all four datasets | AUC/RMSE | Helped |
| Spike-and-slab prior over NN weights with EP prior refinement | SBDT · DBLP | posterior selection of weights | Helped |
Closest papers by meaning
- Factor Augmented Tensor-on-Tensor Neural Networks (AAAI 2025 · similarity 0.87)
- Geometry-aware training of factorized layers in tensor Tucker format (NeurIPS 2024 · similarity 0.87)
- Convolutions and More as Einsum: A Tensor Network Perspective with Advances for Second-Order Methods (NeurIPS 2024 · similarity 0.85)
- Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data (ICLR 2024 · similarity 0.85)
- Streaming Bayes GFlowNets (NeurIPS 2024 · similarity 0.85)
- Don’t Think It Twice: Exploit Shift Invariance for Efficient Online Streaming Inference of CNNs (AAAI 2025 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.