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
In this paper, we develop a Gaussian continuation framework for deep learning, which is an optimization strategy that involves smoothing the loss function by convolving it with a Gaussian kernel. The width of the kernel is decreased over the optimization steps leading to the degree of smoothing being gradually relaxed during training. This enables gradient-based optimization to more easily traverse suboptimal local minima in non-convex loss landscapes. We carefully study the unique theoretical difficulties for continuation posed by deep learning applications, and how classical assumptions made in theoretical analysis of continuation methods must be revised or qualified. Our analysis shows that if the width of the Gaussian kernel is treated as an optimization variable, it naturally tends to zero in virtually all minimization problems. As a consequence, Gaussian continuation will converge to the minima of the original loss function as long as the base optimizer is capable of escaping saddle points. We demonstrate that Gaussian continuation outperforms baseline methods in training a regression network and a convolutional generative adversarial network (GAN).
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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.003 | 0.001 |
| 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