{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:36:51Z","timestamp":1761176211800,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Deep learning models, albeit having remarkable performance in various image and vision tasks, are notoriously parameter-inefficient. The huge number of parameters in these models makes them unsuitable for large-scale deployment on resource-constrained devices due to high memory and computational requirements. It has also been observed in the literature that many parameters in such deep models are redundant. Hence, there is a need to extract parameter-efficient deep models for wide-scale deployment on resource-constrained devices. To address this challenge, we propose a novel parameter reduction framework using Reinforcement Learning (RL) that performs layer-wise structured pruning of deep models. The proposed method leverages the Proximal Policy Optimization (PPO) strategy to assign learnable importance scores to structural units like filters and neurons across all layers of a trained deep model. These scores get sampled from a Bernoulli distribution to determine the pruning decisions. The PPO agents learn using accuracy-aware rewards from a downstream classification task. Additionally, we propose a compression slider mechanism that enables user-defined control over compression rates, ensuring balanced sparsity. Experiments on benchmark models such as VGG16 and AlexNet across two datasets demonstrate that our method achieves better trade-offs between model performance and compression, while offering finer control over pruning granularity. For VGG16 over CIFAR-10, we achieve a total compression of 85.14% with a drop of 2.08% in inference accuracy. Also, for AlexNet, we prune the model by 77.67%, while observing a drop of 2.84% in accuracy. Moreover, our proposed approach enables precise control over the pruning process, allowing for flexible and targeted compression strategies suitable for real-world deployment.<\/jats:p>","DOI":"10.3233\/faia251114","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:51:48Z","timestamp":1761126708000},"source":"Crossref","is-referenced-by-count":0,"title":["Reinforcement Learning-Driven Model Compression: Structured Layer-Wise Parameter Pruning"],"prefix":"10.3233","author":[{"given":"Ashmit","family":"Gupta","sequence":"first","affiliation":[{"name":"Department of Mathematics, Indian Institute of Technology Ropar, Punjab, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anany","family":"Dhamija","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Indian Institute of Technology Ropar, Punjab, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manik","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Indian Institute of Technology Ropar, Punjab, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Parul","family":"Kukrety","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Punjab, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shashi Shekhar","family":"Jha","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Punjab, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sudeepta","family":"Mishra","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Punjab, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251114","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:51:48Z","timestamp":1761126708000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251114"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251114","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}