Research map / Learning theory and algorithms / Matrix and tensor methods
Fast Tucker Rank Reduction for Non-Negative Tensors Using Mean-Field Approximation
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.
Closest papers by meaning
- Fitting Low-Rank Tensors in Constant Time (NeurIPS 2017 · similarity 0.90)
- Low-Rank Tucker Decomposition of Large Tensors Using TensorSketch (NeurIPS 2018 · similarity 0.89)
- Many-body Approximation for Non-negative Tensors (NeurIPS 2023 · similarity 0.88)
- Statistical mechanics of low-rank tensor decomposition (NeurIPS 2018 · similarity 0.88)
- Singleshot : a scalable Tucker tensor decomposition (NeurIPS 2019 · similarity 0.88)
- Fast Tensor Completion via Approximate Richardson Iteration (ICML 2025 · similarity 0.86)
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.
