Papers with network pruning techniques
More Parameters? No Thanks! (2021.findings-acl)
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| Challenge: | Using network pruning, we find that there are large redundancies in MNMT models. |
| Approach: | They propose a method to prune and retrain redundant parameters of an MNMT model to improve bilingual representations while retaining multilinguality. |
| Outcome: | The proposed method improves bilingual representations while retaining multilinguality. |
Rethinking Network Pruning – under the Pre-train and Fine-tune Paradigm (2021.naacl-main)
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| Challenge: | Existing pruning results on benchmark transformers, such as BERT, are not as remarkable as those of convolutional neural networks. |
| Approach: | They propose to apply a knowledge-aware pruning process to transformer-based pre-trained language models to reduce model size and model weight. |
| Outcome: | The proposed pruning method outperforms the leading competitors with a 20-times weight/FLOPs compression and neglectable loss in prediction accuracy. |
Hierarchical Safety Realignment: Lightweight Restoration of Safety in Pruned Large Vision-Language Models (2025.findings-acl)
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| Challenge: | Recent work has shown that pruning can reduce model performance, but it can also lead to degradation in safety performance. |
| Approach: | They propose a hierarchical safety realignment approach to prune large vision-Language Models . they quantify contribution of each attention head to safety and restore neurons . |
| Outcome: | The proposed approach achieves significant safety improvements in LVLMs pruned post pruning. |
EFTNAS: Searching for Efficient Language Models in First-Order Weight-Reordered Super-Networks (2024.lrec-main)
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| Challenge: | Depending on the size of transformer-based models, they can be restricted from deployment in resource-constrained environments. |
| Approach: | They propose to combine neural architecture search and network pruning techniques to generate and train weight-sharing super-networks that contain efficient transformer-based models. |
| Outcome: | The proposed model achieves high-performing, high-performance subnetworks on the general language understanding evaluation and the Stanford Question Answering Dataset. |