An End-to-End Trainable Deep Convolutional Neuro-Fuzzy Classifier
Bibliographic record
Abstract
A key challenge in artificial intelligence is the well-known tradeoff between the interpretability of an algorithm, and its accuracy. Designing interpretable, highly accurate AI models is considered essential to broad acceptance of AI technology, and is the focus of the eXplainable Artificial Intelligence (XAI) community. We report on the design of a new deep neural network that achieves improved interpretability without sacrificing accuracy. Our design is a hybrid deep learning algorithm based in part upon fuzzy logic, which performs as accurately as existing convolutional neural networks. The network is an end-to-end trainable deep convolutional network, which replaces the final dense layers (the classifier component) with a modified ANFIS. We exploit the transparency of fuzzy logic by deriving explanations, in the form of saliency maps, based on the fuzzy rules learned in the ANFIS component.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.005 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".