{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T07:43:17Z","timestamp":1763192597027,"version":"3.45.0"},"reference-count":40,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T00:00:00Z","timestamp":1751241600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,30]],"date-time":"2025-06-30T00:00:00Z","timestamp":1751241600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100002322","name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,6,30]]},"DOI":"10.1109\/ijcnn64981.2025.11228155","type":"proceedings-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T18:46:15Z","timestamp":1763145975000},"page":"1-8","source":"Crossref","is-referenced-by-count":0,"title":["Pruning Everything, Everywhere, All at Once"],"prefix":"10.1109","author":[{"given":"Gustavo Henrique","family":"do Nascimento","sequence":"first","affiliation":[{"name":"Universidade de São Paulo,Escola Politécnica,Brazil"}]},{"given":"Ian","family":"Pons","sequence":"additional","affiliation":[{"name":"Universidade de São Paulo,Escola Politécnica,Brazil"}]},{"given":"Anna Helena","family":"Reali Costa","sequence":"additional","affiliation":[{"name":"Universidade de São Paulo,Escola Politécnica,Brazil"}]},{"given":"Artur","family":"Jordao","sequence":"additional","affiliation":[{"name":"Universidade de São Paulo,Escola Politécnica,Brazil"}]}],"member":"263","reference":[{"key":"ref1","article-title":"Artificial intelligence index report 2025","author":"M","year":"2025","journal-title":"Tech. Rep"},{"key":"ref2","article-title":"International ai safety report","author":"B","year":"2025","journal-title":"Tech. Rep"},{"key":"ref3","article-title":"Holistically evaluating the environmental impact of creating language models","author":"M","year":"2025","journal-title":"ICLR"},{"key":"ref4","article-title":"The llama 3 herd of models","author":"D","year":"2024","journal-title":"ArXiv"},{"key":"ref5","article-title":"A survey on deep neural network pruning: Taxonomy, comparison, analysis, and recommendations","author":"C","year":"2024","journal-title":"TPAMI"},{"doi-asserted-by":"publisher","key":"ref6","DOI":"10.1109\/tpami.2023.3334614"},{"doi-asserted-by":"publisher","key":"ref7","DOI":"10.1007\/978-3-031-78169-8_28"},{"key":"ref8","article-title":"Layermerge: Neural network depth compression through layer pruning and merging","author":"K","year":"2024","journal-title":"ICML"},{"key":"ref9","article-title":"Similarity of neural network representations revisited","author":"Kornblith","year":"2019","journal-title":"ICML"},{"key":"ref10","article-title":"Compact language models via pruning and knowledge distillation","author":"M","year":"2024","journal-title":"NeurIPS"},{"key":"ref11","article-title":"Structural pruning via latency-saliency knapsack","author":"Shen","year":"2022","journal-title":"NeurIPS"},{"doi-asserted-by":"publisher","key":"ref12","DOI":"10.1109\/CVPR52733.2024.01520"},{"key":"ref13","article-title":"Quantifying the carbon emissions of machine learning","author":"Lacoste","year":"2019","journal-title":"NeurIPS"},{"doi-asserted-by":"publisher","key":"ref14","DOI":"10.1109\/CVPR52733.2024.01523"},{"doi-asserted-by":"publisher","key":"ref15","DOI":"10.1109\/CVPR52733.2024.01530"},{"doi-asserted-by":"publisher","key":"ref16","DOI":"10.18653\/v1\/2024.findings-emnlp.372"},{"key":"ref17","article-title":"The unreasonable ineffectiveness of the deeper layers","author":"Gromov","year":"2025","journal-title":"ICLR"},{"key":"ref18","article-title":"Shortened LLaMA: A simple depth pruning for large language models","author":"K","year":"2024","journal-title":"ICLRW"},{"doi-asserted-by":"publisher","key":"ref19","DOI":"10.1109\/CVPR52688.2022.00029"},{"doi-asserted-by":"publisher","key":"ref20","DOI":"10.1007\/11564089_7"},{"doi-asserted-by":"publisher","key":"ref21","DOI":"10.1109\/ICCVW60793.2023.00130"},{"doi-asserted-by":"publisher","key":"ref22","DOI":"10.1109\/CVPR42600.2020.00153"},{"key":"ref23","article-title":"What makes a good prune? maximal unstructured pruning for maximal cosine similarity","author":"G","year":"2024","journal-title":"ICLR"},{"doi-asserted-by":"publisher","key":"ref24","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref25","article-title":"A simple and effective pruning approach for large language models","author":"Sun","year":"2024","journal-title":"ICLR"},{"doi-asserted-by":"publisher","key":"ref26","DOI":"10.1109\/TPAMI.2018.2874634"},{"doi-asserted-by":"publisher","key":"ref27","DOI":"10.1109\/TNNLS.2021.3059529"},{"doi-asserted-by":"publisher","key":"ref28","DOI":"10.1109\/TNNLS.2022.3147269"},{"key":"ref29","article-title":"Pruning networks with cross-layer ranking & k-reciprocal nearest filters","author":"L","year":"2023","journal-title":"TNNLS"},{"key":"ref30","article-title":"DAIS: automatic channel pruning via differentiable annealing indicator search","author":"G","year":"2023","journal-title":"TPAMI"},{"key":"ref31","article-title":"On the channel pruning using graph convolution network for convolutional neural network acceleration","author":"J","year":"2022","journal-title":"IJCAI"},{"doi-asserted-by":"publisher","key":"ref32","DOI":"10.1109\/CVPR52688.2022.01203"},{"key":"ref33","article-title":"Revisit kernel pruning with lottery regulated grouped convolutions","author":"Zhong","year":"2022","journal-title":"ICLR"},{"key":"ref34","article-title":"Generalized shape metrics on neural representations","author":"Williams","year":"2021","journal-title":"NeurIPS"},{"key":"ref35","article-title":"Representational dissimilarity metric spaces for stochastic neural networks","author":"Duong","year":"2023","journal-title":"ICLR"},{"key":"ref36","article-title":"Adaptive sharpness-aware pruning for robust sparse networks","author":"Bair","year":"2024","journal-title":"ICLR"},{"key":"ref37","article-title":"Benchmarking neural network robustness to common corruptions and perturbations","author":"Hendrycks","year":"2019","journal-title":"ICLR"},{"key":"ref38","article-title":"Harder or different? a closer look at distribution shift in dataset reproduction","author":"L","year":"2020","journal-title":"ICML"},{"doi-asserted-by":"publisher","key":"ref39","DOI":"10.1109\/TNNLS.2022.3162067"},{"doi-asserted-by":"publisher","key":"ref40","DOI":"10.1016\/j.neucom.2020.04.151"}],"event":{"name":"2025 International Joint Conference on Neural Networks (IJCNN)","start":{"date-parts":[[2025,6,30]]},"location":"Rome, Italy","end":{"date-parts":[[2025,7,5]]}},"container-title":["2025 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11227166\/11227148\/11228155.pdf?arnumber=11228155","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T07:39:38Z","timestamp":1763192378000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11228155\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,30]]},"references-count":40,"URL":"https:\/\/doi.org\/10.1109\/ijcnn64981.2025.11228155","relation":{},"subject":[],"published":{"date-parts":[[2025,6,30]]}}}