INTERPRETABLE LEARNING-BASED MULTI-MODAL HASHING ANALYSIS FOR MULTI-VIEW FEATURE REPRESENTATION LEARNING
Bibliographic record
Abstract
In this work, an interpretable learning-based multi-modal hashing analysis (ILMMHA) model is proposed with appli-cation to multi-view feature representation learning. In the proposed model, a cascade network structure is first utilized to reveal the intrinsically semantic representation of input variables. Then, a multi-modal hashing (MMH) method is integrated with the explored semantic representation, gener-ating an interpretable learning-based model for multi-view feature representation. Since MMH is capable of measuring semantic similarity across multiple variables jointly, it provides a natural link between the explored intrinsically semantic representation and its similarity across multi-modal data/information. Benefiting from integration of the cascade structure and MMH, the ILMMHA model leads to a new multi-view feature representation of high quality. To demonstrate the effectiveness and generic nature of the ILMMHA model, we conduct experiments on the cross-modal based audio-visual emotion and text-image recognition tasks, respectively. Experimental results demonstrate the superiority of the proposed model on multi-view feature representation learning.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".