Modout: Learning to Fuse Modalities via Stochastic Regularization
Why this work is in the frame
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Bibliographic record
Abstract
Model selection methods based on stochastic regularization suchas Dropout have been widely used in deep learning due to theirsimplicity and effectiveness. The standard Dropout method treatsall units, visible or hidden, in the same way, thus ignoring any a prioriinformation related to grouping or structure. Such structure ispresent in multi-modal learning applications, where subsets of unitsmay correspond to individual modalities. In this abstract we describeModout, a model selection method based on stochastic regularization,which is particularly useful in the multi-modal setting.Different from previous methods, it is capable of learning whetheror when to fuse two modalities in a layer. Evaluation of Modouton the Montalbano gesture recognition dataset demonstrates improvedperformance compared to other stochastic regularizationmethods, and is on par with a state-of-the-art carefully designedfusion architecture.
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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 | 0.001 |
| 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