Stochastic Receptive Fields in Deep Convolutional Networks
Bibliographic record
Abstract
<p>Deep convolutional neural networks (ConvNets) have rapidly grown<br />in popularity due to their powerful capabilities in representing and<br />modelling the high-level abstraction of complex data. However,<br />ConvNets require an abundance of data to adequately train network<br />parameters. To tackle this problem, we introduce the concept<br />of stochastic receptive fields, where the receptive fields are<br />stochastic realizations of a random field that obey a learned distribution.<br />We study the efficacy of incorporating layers of stochastic<br />receptive fields to a ConvNet to boost performance without the<br />need for additional training data. Preliminary results showing an<br />improvement in accuracy ( 2% drop in test error) was achieved by<br />adding a layer of stochastic receptive fields to a ConvNet compared<br />to adding a layer of fully-trained receptive fields, when training with<br />a small training set consisting of 20% of the STL-10 dataset.</p>
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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 | 0.000 | 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 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".