Elimination-compensation pruning for fully-connected neural networks
arXiv:2602.20467v1 Announce Type: new Abstract: The unmatched ability of Deep Neural Networks in capturing complex patterns in large and noisy datasets is often associated with their large hypothesis space, and consequently to the vast amount of parameters that characterize model architectures. Pruning techniques affirmed themselves as valid tools to extract sparse representations of neural networks parameters, carefully balancing between compression and preservation of information. However, a fundamental assumption behind pruning is that expendable weights should have small impact on the error of the network, while highly important weights should tend to have a larger influence on the inference. We argue that this idea could be generalized; what if a weight is not simply removed but also compensated with a perturbation of the adjacent bias, which does not contribute to the network sparsity? Our work introduces a novel pruning method in which the importance measure of each weight is c
arXiv:2602.20467v1 Announce Type: new Abstract: The unmatched ability of Deep Neural Networks in capturing complex patterns in large and noisy datasets is often associated with their large hypothesis space, and consequently to the vast amount of parameters that characterize model architectures. Pruning techniques affirmed themselves as valid tools to extract sparse representations of neural networks parameters, carefully balancing between compression and preservation of information. However, a fundamental assumption behind pruning is that expendable weights should have small impact on the error of the network, while highly important weights should tend to have a larger influence on the inference. We argue that this idea could be generalized; what if a weight is not simply removed but also compensated with a perturbation of the adjacent bias, which does not contribute to the network sparsity? Our work introduces a novel pruning method in which the importance measure of each weight is computed considering the output behavior after an optimal perturbation of its adjacent bias, efficiently computable by automatic differentiation. These perturbations can be then applied directly after the removal of each weight, independently of each other. After deriving analytical expressions for the aforementioned quantities, numerical experiments are conducted to benchmark this technique against some of the most popular pruning strategies, demonstrating an intrinsic efficiency of the proposed approach in very diverse machine learning scenarios. Finally, our findings are discussed and the theoretical implications of our results are presented.
Executive Summary
This article introduces a novel pruning method for fully-connected neural networks, which considers the output behavior after an optimal perturbation of the adjacent bias when computing the importance measure of each weight. The method, called elimination-compensation pruning, demonstrates intrinsic efficiency in various machine learning scenarios through numerical experiments. The technique efficiently computes perturbations using automatic differentiation and applies them after weight removal, showcasing its potential as a valid tool for extracting sparse representations of neural networks while balancing compression and information preservation.
Key Points
- ▸ Introduction of a novel pruning method called elimination-compensation pruning
- ▸ Computation of importance measure considering output behavior after optimal perturbation of adjacent bias
- ▸ Efficient computation of perturbations using automatic differentiation
Merits
Efficient Pruning
The proposed method efficiently prunes neural networks while preserving information, making it a valuable tool for model compression and sparse representation.
Demerits
Computational Overhead
The method's reliance on automatic differentiation and perturbation computations may introduce additional computational overhead, potentially impacting its scalability for large-scale neural networks.
Expert Commentary
The introduction of elimination-compensation pruning marks a significant advancement in neural network compression techniques. By considering the output behavior after optimal perturbation of the adjacent bias, this method provides a more nuanced understanding of weight importance, enabling more effective pruning. However, further research is necessary to fully explore the method's scalability and applicability to diverse neural network architectures and applications. The potential implications of this work are substantial, as it can contribute to the development of more efficient, compact, and interpretable neural network models, ultimately driving progress in artificial intelligence research and its practical applications.
Recommendations
- ✓ Further investigation into the method's scalability and applicability to various neural network architectures and applications
- ✓ Exploration of the method's potential integration with other neural network compression techniques to achieve even greater efficiency and performance gains
