Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Tensor decomposition (TD) has been recognized as an effective technique for multilinear dimensionality reduction and feature extraction for decades. However, traditional TD approaches often struggle to capture complex hierarchical structures and nonlinear relationships in high-dimensional datasets. For instance, in biomedical settings, disease groups may naturally contain subgroups or exhibit hierarchical structures; mechanistic interactions among diseases, drugs and targets often demonstrate nonlinearity. To address these challenges, a new paradigm, deep tensor decomposition (deep TD) has recently emerged inspired by the success of deep learning. Deep TD techniques can be mainly divided into two categories: linear and nonlinear deep TD. Linear deep TD exploits the layered structure of deep neural networks (DNNs) to recursively factorize factor matrices obtained from the classic TD enabling feature extraction at multiple levels of granularity. Nonlinear deep TD leverages the expressive power of DNNs to capture nonlinear correlations within the data. Despite rapid progress, there remains no unified treatment of deep TD methods. In this survey, we provide a comprehensive review of deep TD models, together with the deep learning training schemes for TD, and applications of deep TD models. Finally, we discuss open challenges and outline promising directions for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it