Multi-Energy Quasi-Symplectic Langevin Inference for Latent Disentangled Learning
Bibliographic record
Abstract
The variational autoencoder-based method has been widely used for modeling massive datasets. However, for 3D images, simultaneously achieving disentangled representations, low-variance Evidence Lower Bounds (ELBO), and a lightweight model remains a challenging task. In this work, we propose a Langevin dynamics-based inference framework that integrates target data information for efficient likelihood inference and disentangles appearance and morphology features via multi-scale energy-level encoding that enables unsupervised disentanglement. We adopt a quasi-symplectic integrator to handle the Hessian-related computational bottleneck that often arises in Langevin-based flow inference. We demonstrate both theoretical and empirical effectiveness of our approach compared to other methods. Experiments on public benchmarks and clinical 3D imaging datasets show that our Langevin-VAE achieves high-quality generation and learns disentangled shape and appearance representations with a model size of only 1.7M parameters. The code will be available at: https://github.com/LaplaceCenter/LangevinVAE.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".