Bibliographic record
Abstract
Deep neural networks (DNNs) have achieved significant performance improvement in image classification tasks.A DNN utilizes an enormous number of trainable parameters and non-linear functions to learn a mapping from a set of input images to a set of target classes. However, the increasing number of parameters, called the overparameterization problem, can create major challenges in implementation of these models on resource limited devices and can encourage overfitting of the model. Pruning less important trainable parameters in DNNs is one of the major established methods to defeat the above challenges. Pruning methods are also applicable on small models by design to further reduce their size. This thesis proposes the concept of energy models for pruning convolutional filters and hidden units with corresponding incoming and outgoing connections and bias terms for image classification tasks. Specifically, two energy models, namely EPruning and IPruning, are proposed. The first approach uses the Hamiltonian of a given DNN to form an energy loss minimization problem. The loss function is defined based on the difference between the energy of target class and the closest energy of the competitive classes. The second approach uses entropy of feature maps, Kullback–Leibler (KL) divergence between convolution filters in a layer, and activation level of hidden units to form an Ising energy-based minimization problem for detecting less important and redundant parameters in the network. Despite most pruning methods, which are based on defining a pruning threshold per layer, the proposed methods are data-driven and perform a network-wise pruning of the trainable parameters. We define pruning as searching for a sub-network of a given DNN, which has the lowest energy loss value based on a defined energy loss function. Since this is an NP-hard combinatorial optimization problem, a population-based global optimization framework is proposed to minimize the pruning objective functions. The proposed framework is evaluated on different flavors of DNNs (e.g. ResNets, AlexNet, and SqueezeNet) and datasets (e.g. CIFAR-10, CIFAR-100, and ImageNet) for image classification tasks.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".