  | OLIVEIRA, I., MOCANU, D. C., & LEIVA, L. A. (2025). Sparse-to-Sparse Training of Diffusion Models. Transactions on Machine Learning Research.  Peer Reviewed verified by ORBi |
 | Onur Yildirim, M., Ceren Gok Yildirim, E., MOCANU, D. C., & Vanschoren, J. (11 August 2025). Self-Regulated Neurogenesis for Online Data-Incremental Learning [Paper presentation]. CoLLAs 2025: Conference on Lifelong Learning Agents, Philadelphia, United States. Peer reviewed |
 | van der Wal, P. R. D., Strisciuglio, N., Azzopardi, G., & MOCANU, D. C. (July 2025). Multilayer perceptron ensembles in a truly sparse training context. Neural Computing and Applications, 37 (20), 15419 - 15438. doi:10.1007/s00521-025-11294-3 Peer Reviewed verified by ORBi |
 | Xiao, Q., WU, B., Poddubnyy, A., Mocanu, E., H. Nguyen, P., Pechenizkiy, M., & MOCANU, D. C. (2025). Addressing the Collaboration Dilemma in Low-Data Federated Learning via Transient Sparsity. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/67632. |
 | Xiao, Q., Ansell, A., WU, B., Yin, L., Pechenizkiy, M., Liu, S., & MOCANU, D. C. (2025). Leave it to the Specialist: Repair Sparse LLMs with Sparse Fine-Tuning via Sparsity Evolution. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/67633. |
 | Muslimani, C., Grooten, B., Ranganatha Sastry Mamillapalli, D., Pechenizkiy, M., MOCANU, D. C., & Taylor, M. E. (2025). Boosting Robustness in Preference-Based Reinforcement Learning with Dynamic Sparsity. In AAMAS 2025: Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems (pp. 2687–2689). Detroit, United States: International Foundation for Autonomous Agents and Multiagent Systems. Peer reviewed |
 | WU, B.* , Xiao, Q.* , Wang, S., Strisciuglio, N., Pechenizkiy, M., Keulen, M. V., MOCANU, D. C., & Mocanu, E. (15 May 2025). Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness [Paper presentation]. ICLR 2025, Singapore, Singapore. Peer reviewed* These authors have contributed equally to this work. |
 | WU, B., Xiao, Q., Liu, S., Yin, L., Pechenizkiy, M., MOCANU, D. C., Van Keulen, M., & Mocanu, E. (10 December 2024). E2ENet: Dynamic Sparse Feature Fusion for Accurate and Efficient 3D Medical Image Segmentation [Paper presentation]. NeurIPS 2024: Thirty-Eighth Annual Conference on Neural Information Processing Systems, Vancouver, Canada. doi:10.52202/079017-3762 Peer reviewed |
 | Xiao, Q., WU, B., Yin, L., GADZINSKI, C., Huang, T., Pechenizkiy, M., & MOCANU, D. C. (November 2024). Are Sparse Neural Networks Better Hard Sample Learners? [Paper presentation]. BMVC 2024: The 35th British Machine Vision Conference, Glasgow, United Kingdom. Peer reviewed |
 | Atashgahi, Z., Liu, T., Pechenizkiy, M., Veldhuis, R., MOCANU, D. C., & van der Schaar, M. (2024). Unveiling the Power of Sparse Neural Networks for Feature Selection. In U. Endriss (Ed.), ECAI 2024 - 27th European Conference on Artificial Intelligence, Including 13th Conference on Prestigious Applications of Intelligent Systems, PAIS 2024, Proceedings. IOS Press BV. doi:10.3233/FAIA240799 Peer reviewed |
 | Oerlemans, C., Grooten, B., Braat, M., Alassi, A., Silvas, E., & MOCANU, D. C. (10 October 2024). LiMTR: Time Series Motion Prediction for Diverse Road Users through Multimodal Feature Integration [Paper presentation]. NeurIPS 2024 Workshop - Time Series in the Age of Large Models, Vancouver, Canada. Peer reviewed |
  | Xiao, Q., WU, B., Pechenizkiy, M., & MOCANU, D. C. (10 September 2024). Achieving Long-term Time Series Forecasting Models with Fewer Than 1k Parameters through Dynamic Sparse Training [Poster presentation]. ECML PKDD 2024: Machine Learning for Sustainable Power Systems Workshop, Vilnius, Lithuania. Peer reviewed |
 | WU, B., Keulen, M. V., MOCANU, D. C., & Mocanu, E. (10 September 2024). Insights into Dynamic Sparse Training: Theory Meets Practice [Poster presentation]. ECML PKDD, Vilnius, Lithuania. Peer reviewed |
 | Xiao, Q., Ma, P., Fernandez-Lopez, A., WU, B., Yin, L., Petridis, S., Pechenizkiy, M., Pantic, M., MOCANU, D. C., & Liu, S. (2024). Dynamic Data Pruning for Automatic Speech Recognition. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 4488 - 4492. doi:10.21437/Interspeech.2024-1330 Peer reviewed |
 | Grooten, B., Tomilin, T., Vasan, G., Taylor, M. E., Mahmood, R. A., Fang, M., Pechenizkiy, M., & MOCANU, D. C. (2024). MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning. In AAMAS '24: Proceedings of the 2024 International Conference on Autonomous Agents and Multiagent Systems. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC. Peer reviewed |
 | Liu, K., Atashgahi, Z., Sokar, G., Pechenizkiy, M., & MOCANU, D. C. (2024). Supervised Feature Selection via Ensemble Gradient Information from Sparse Neural Networks. In AISTATS 2024: Proceedings of The 27th International Conference on Artificial Intelligence and Statistics (pp. 3952-3960). Proceedings of Machine Learning Research. Peer reviewed |
 | Atashgahi, Z., Pechenizkiy, M., Veldhuis, R., & MOCANU, D. C. (2024). Adaptive Sparsity Level during Training for Efficient Time Series Forecasting with Transformers. In ECMLPKDD 2024: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. LNCS. doi:10.1007/978-3-031-70341-6_1 Peer reviewed |
 | Yildirim, M. O., Gok Yildirim, E. C., Sokar, G., MOCANU, D. C., & Vanschoren, J. (20 November 2023). Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates [Paper presentation]. CPAL 2024: Conference on Parsimony and Learning. Peer reviewed |
 | Grooten, B., Sokar, G., Dohare, S., Mocanu, E., Taylor, M. E., Pechenizkiy, M., & MOCANU, D. C. (2023). Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning. In AAMAS '23: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems. International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC. doi:10.5555/3545946.3598862 Peer reviewed |
 | Atashgahi, Z., Zhang, X., Kichler, N., Liu, S., Yin, L., Pechenizkiy, M., Veldhuis, R., & MOCANU, D. C. (2023). Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks. Transactions on Machine Learning Research. Peer Reviewed verified by ORBi |
 | Liu, S., Chen, T., Chen, X., Chen, X., Xiao, Q., WU, B., Kärkkäinen, T., Pechenizkiy, M., MOCANU, D. C., & Wang, Z. (01 February 2023). More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity [Paper presentation]. ICLR 2023: The Eleventh International Conference on Learning Representations. Peer reviewed |
 | Miras, K., MOCANU, D. C., & Eiben, A. E. (13 January 2023). Hu-bot: promoting the cooperation between humans and mobile robots. Neural Computing and Applications, 35 (23), 16841 - 16852. doi:10.1007/s00521-022-08061-z Peer Reviewed verified by ORBi |
 | Nowak, A. I., Grooten, B., MOCANU, D. C., & Tabor, J. (2023). Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training [Paper presentation]. NeurIPS 2023: Thirty-seventh Annual Conference on Neural Information Processing Systems. Peer reviewed |
 | Xiao, Q.* , WU, B.* , Zhang, Y., Liu, S., Pechenizkiy, M., Mocanu, E., & MOCANU, D. C. (10 December 2022). Dynamic Sparse Network for Time Series Classification: Learning What to" see'' [Paper presentation]. Advances in Neural Information Processing Systems 35, NeurIPS 2022, New Orleans, United States. Peer reviewed* These authors have contributed equally to this work. |
 | MOCANU, D. C., Mocanu, E., Stone, P., Nguyen, P. H., Gibescu, M., & Liotta, A. (19 June 2018). Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science. Nature Communications, 9 (1), 2383. doi:10.1038/s41467-018-04316-3 Peer Reviewed verified by ORBi |
