Parametric Exponential Linear Unit for Deep Convolutional Neural\n Networks
Bibliographic record
Abstract
Object recognition is an important task for improving the ability of visual\nsystems to perform complex scene understanding. Recently, the Exponential\nLinear Unit (ELU) has been proposed as a key component for managing bias shift\nin Convolutional Neural Networks (CNNs), but defines a parameter that must be\nset by hand. In this paper, we propose learning a parameterization of ELU in\norder to learn the proper activation shape at each layer in the CNNs. Our\nresults on the MNIST, CIFAR-10/100 and ImageNet datasets using the NiN,\nOverfeat, All-CNN and ResNet networks indicate that our proposed Parametric ELU\n(PELU) has better performances than the non-parametric ELU. We have observed as\nmuch as a 7.28% relative error improvement on ImageNet with the NiN network,\nwith only 0.0003% parameter increase. Our visual examination of the non-linear\nbehaviors adopted by Vgg using PELU shows that the network took advantage of\nthe added flexibility by learning different activations at different layers.\n
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.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".