{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T07:32:48Z","timestamp":1763191968544,"version":"3.45.0"},"reference-count":30,"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"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,6,30]]},"DOI":"10.1109\/ijcnn64981.2025.11228768","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":["Stochastic Pruning for Neural Networks"],"prefix":"10.1109","author":[{"given":"Luis Alfredo","family":"Avenda\u00f1o Mu\u00f1oz","sequence":"first","affiliation":[{"name":"School of Computer Science University of Leeds,Leeds,United Kingdom"}]},{"given":"Netta","family":"Cohen","sequence":"additional","affiliation":[{"name":"School of Computer Science University of Leeds,Leeds,United Kingdom"}]},{"given":"Nabi","family":"Omidvar","sequence":"additional","affiliation":[{"name":"School of Computer Science & Leeds University Business School University of Leeds,Leeds,United Kingdom"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1877","article-title":"Language Models are Few-Shot Learners","volume-title":"Advances in Neural Information Processing Systems","volume":"33","author":"Brown"},{"key":"ref2","doi-asserted-by":"crossref","DOI":"10.21428\/bf6fb269.1f033948","article-title":"The Computational Limits of Deep Learning","volume-title":"Computing within Limits.","author":"Thompson"},{"issue":"1","key":"ref3","first-page":"2383","article-title":"Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science","volume":"9","author":"Mocanu"},{"author":"Dettmers","key":"ref4","article-title":"Sparse Networks from Scratch: Faster Training without Losing Performance"},{"article-title":"The Difficulty of Training Sparse Neural Networks","volume-title":"ICML 2019 Workshop on Identifying and Understanding Deep Learning Phenomena","author":"Evci","key":"ref5"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/72.80236"},{"key":"ref7","article-title":"Skeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment","volume-title":"Advances in Neural Information Processing Systems","volume":"1","author":"Mozer"},{"key":"ref8","article-title":"Optimal Brain Damage","volume-title":"Advances in Neural Information Processing Systems","volume":"2","author":"LeCun"},{"key":"ref9","first-page":"18 098","article-title":"WoodFisher: Efficient Second-Order Approximation for Neural Network Compression","volume-title":"Advances in Neural Information Processing Systems","volume":"33","author":"Singh"},{"article-title":"Layer-adaptive Sparsity for the Magnitude-based Pruning","volume-title":"International Conference on Learning Representations","author":"Lee","key":"ref10"},{"key":"ref11","first-page":"6377","article-title":"Pruning neural networks without any data by iteratively conserving synaptic flow","volume-title":"Advances in Neural Information Processing Systems","volume":"33","author":"Tanaka"},{"article-title":"SNIP:Single-Shot Network Pruning Based on Connection Sensitivity","volume-title":"International Conference on Learning Representations","author":"Lee","key":"ref12"},{"key":"ref13","article-title":"Second order derivatives for network pruning: Optimal Brain Surgeon","volume-title":"Advances in Neural Information Processing Systems","volume":"5","author":"Hassibi"},{"key":"ref14","article-title":"Optimal Brain Surgeon: Extensions and performance comparisons","volume-title":"Advances in Neural Information Processing Systems","volume":"6","author":"Hassibi"},{"key":"ref15","first-page":"747","article-title":"Is Complexity Required for Neural Network Pruning? A Case Study on Global Magnitude Pruning","volume-title":"2024 IEEE Conference on Artificial Intelligence (CAI)","author":"Gupta"},{"article-title":"The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks","volume-title":"International Conference on Learning Representations","author":"Frankle","key":"ref16"},{"issue":"3","key":"ref17","first-page":"8","article-title":"Tiny Machine Learning: Progress and Futures [Feature]","volume":"23","author":"Lin"},{"issue":"6","key":"ref18","first-page":"6577","article-title":"Gradient Flow in Sparse Neural Networks and How Lottery Tickets Win","volume":"36","author":"Evci"},{"key":"ref19","first-page":"6105","article-title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","volume-title":"Proceedings of the 36th International Conference on Machine Learning","author":"Tan"},{"article-title":"Picking Winning Tickets Before Training by Preserving Gradient Flow","volume-title":"International Conference on Learning Representations","author":"Wang","key":"ref20"},{"key":"ref21","first-page":"2943","article-title":"Rigging the lottery: Making all tickets winners","volume-title":"Proceedings of the 37th International Conference on Machine Learning","volume":"119","author":"Evci"},{"key":"ref22","first-page":"3259","article-title":"Linear Mode Connectivity and the Lottery Ticket Hypothesis","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"Frankle"},{"article-title":"The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks","volume-title":"International Conference on Learning Representations","author":"Entezari","key":"ref23"},{"key":"ref24","article-title":"Learning both Weights and Connections for Efficient Neural Network","volume-title":"Advances in Neural Information Processing Systems","volume":"28","author":"Han"},{"volume-title":"The State of Sparsity in Deep Neural Networks","author":"Gale","key":"ref25"},{"key":"ref26","first-page":"6438","article-title":"Towards Compact CNNs via Collaborative Compression","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Li"},{"volume-title":"Adding Gradient Noise Improves Learning for Very Deep Networks","author":"Neelakantan","key":"ref27"},{"issue":"2","key":"ref28","first-page":"182","article-title":"A fast and elitist multiobjective genetic algorithm: NSGA-II","volume":"6","author":"Deb"},{"key":"ref29","first-page":"23965","article-title":"Model soups: Averaging weights of multiple fine-tuned models improves accuracy without increasing inference time","volume-title":"Proceedings of the 39th International Conference on Machine Learning","author":"Wortsman"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58536-5_38"}],"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\/11228768.pdf?arnumber=11228768","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T07:28:29Z","timestamp":1763191709000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11228768\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,30]]},"references-count":30,"URL":"https:\/\/doi.org\/10.1109\/ijcnn64981.2025.11228768","relation":{},"subject":[],"published":{"date-parts":[[2025,6,30]]}}}