Publications

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Legaard, C. M., Schranz, T., Schweiger, G., Drgona, J., Falay, B., Gomes, C., Iosifidis, A., Abkar, M. & Larsen, P. G. (2023). Constructing Neural Network Based Models for Simulating Dynamical Systems. ACM Computing Surveys, 55(11), 1-34. Artikel 236. https://doi.org/10.1145/3567591

Hedegaard Morsing, L. & Iosifidis, A. (2022). Continual 3D Convolutional Neural Networks for Real-time Processing of Videos. I S. Avidan, G. Brostow, M. Cissé, G. M. Farinella & T. Hassner (red.), Computer Vision – ECCV 2022 - 17th European Conference, 2022, Proceedings (s. 369-385). Springer. https://doi.org/10.1007/978-3-031-19772-7_22

Hedegaard, L. & Iosifidis, A. (2023). Continual Inference: A Library for Efficient Online Inference with Deep Neural Networks in PyTorch. I L. Karlinsky, T. Michaeli & K. Nishino (red.), Computer Vision – ECCV 2022 Workshops, Proceedings (s. 21-34). Springer. https://doi.org/10.1007/978-3-031-25082-8_2

Hedegaard Morsing, L., Heidari, N. & Iosifidis, A. (2023). Continual Spatio-Temporal Graph Convolutional Networks. Pattern Recognition, 140(C), Artikel 109528. https://doi.org/10.1016/j.patcog.2023.109528

Hedegaard Morsing, L., Bakhtiarnia, A. & Iosifidis, A. (2022). Continual Transformers: Redundancy-Free Attention for Online Inference. The Eleventh International Conference on Learning Representations. https://arxiv.org/abs/2201.06268

Hedegaard Morsing, L., Bakhtiarnia, A. & Iosifidis, A. (2023). Continual Transformers: Redundancy-Free Attention for Online Inference. https://openreview.net/forum?id=PolHquob8M7

Bakhtiarnia, A., Zhang, Q. & Iosifidis, A. (2023). Crowd Counting on Heavily Compressed Images with Curriculum Pre-Training. I 2023 IEEE Symposium Series on Computational Intelligence (SSCI) (s. 559-564). IEEE. https://doi.org/10.1109/SSCI52147.2023.10371805

Minh Nguyen, Q., Tran, D. T., Kanniainen, J., Iosifidis, A. & Gabbouj, M. (2024). Cryptocurrency Portfolio Optimization by Neural Networks. I 2023 IEEE Symposium Series on Computational Intelligence (SSCI) (s. 25-32). IEEE. https://doi.org/10.1109/SSCI52147.2023.10371855

Panagiotatos, G., Passalis, N., Iosifidis, A., Gabbouj, M. & Tefas, A. (2019). Curriculum-based Teacher Ensemble for Robust Neural Network Distilation. I 27th European Signal Processing Conference EUSIPCO 2019 IEEE. http://eusipco2019.org/Proceedings/papertitles.html

Panagiotatos, G., Passalis, N., Iosifidis, A., Gabbouj, M. & Tefas, A. (2019). Curriculum-based teacher ensemble for robust neural network distillation. I 2019 27th European Signal Processing Conference (EUSIPCO) IEEE. https://doi.org/10.23919/EUSIPCO.2019.8903112

Iosifidis, A. (2020). Danish researchers develop artificial intelligence to expose stock-market fraud. I J. Bruun (red.), Profile 2020 (s. 94-95). Aarhus Universitet, Institut for Ingeniørvidenskab . https://www.e-pages.dk/aarhusuniversitet/2231/html5/

Böttjer, T., Ørnskov Rønsch, G., Gonçalves Gomes, C. Â., Ramanujan, D., Iosifidis, A. & Larsen, P. G. (2021). Data-Driven Identification of Remaining Useful Life for Plastic Injection Moulds. I A.-L. Andersen, R. Andersen, D. Brunoe, M. Stoettrup Schioenning Larsen, K. Nielsen, A. Napoleone & S. Kjeldgaard (red.), Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems: Proceedings of the Changeable, Agile, Reconﬁgurable and Virtual Production Conference and the World Mass Customization & Personalization Conference (s. 431-439). Springer. https://www.springerprofessional.de/en/data-driven-identification-of-remaining-useful-life-for-plastic-/19816878

Böttjer, T., Ørnskov Rønsch, G., Gomes, C., Ramanujan, D., Iosifidis, A. & Larsen, P. G. (2022). Data-Driven Identification of Remaining Useful Life for Plastic Injection Moulds. I A.-L. Andersen, R. Andersen, T. D. Brunoe, M. Stoettrup Schioenning Larsen, K. Nielsen, A. Napoleone & S. Kjeldgaard (red.), Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems - Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference CARV 2021 and 10th World Mass Customization and Personalization Conference MCPC 2021 (s. 431-439). Springer. https://doi.org/10.1007/978-3-030-90700-6_49

Thanh Tran, D., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2019). Data-driven Neural Architecture Learning for Financial Time-series Forecasting. I Digital Image and Signal Processing https://arxiv.org/ftp/arxiv/papers/1903/1903.06751.pdf

Amarloo, A., Cinnella, P., Iosifidis, A., Forooghi, P. & Abkar, M. (2023). Data-driven Reynolds stress models based on the frozen treatment of Reynolds stress tensor and Reynolds force vector. Physics of Fluids, 35(7), Artikel 075154. https://doi.org/10.1063/5.0160977

Raitoharju, J., Riabchenko, E., Meissner, K., Ahmad, I., Iosifidis, A., Gabbouj, M. & Kiranyaz, S. (2017). Data Enrichment in Fine-Grained Classification of Aquatic Macroinvertebrates. I Proceedings - 2nd Workshop on Computer Vision for Analysis of Underwater Imagery, CVAUI 2016 - In Conjunction with International Conference on Pattern Recognition, ICPR 2016 (s. 43-48). Artikel 7813092 IEEE. https://doi.org/10.1109/CVAUI.2016.20

Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2019). Deep Adaptive Input Normalization for Time Series Forecasting. arxiv.org. https://arxiv.org/pdf/1902.07892.pdf

Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2020). Deep Adaptive Input Normalization for Time Series Forecasting. IEEE Transactions on Neural Networks and Learning Systems, 31(9), 3760-3765. https://doi.org/10.1109/TNNLS.2019.2944933

Høye, T. T., Ärje, J., Bjerge, K., Hansen, O. L. P., Iosifidis, A., Leese, F., Mann, H. M. R., Meissner, K., Melvad, C. & Raitoharju, J. (2021). Deep learning and computer vision will transform entomology. Proceedings of the National Academy of Sciences (PNAS), 118(2), Artikel e2002545117. https://doi.org/10.1073/pnas.2002545117

Iosifidis, A. & Tefas, A. (2020). Deep Learning for Visual Content Analysis. Signal Processing: Image Communication, 83(April), Artikel 115806. https://doi.org/10.1016/j.image.2020.115806

Pauwels, R. & Iosifidis, A. (2024). Deep Learning in Image Processing: Part 1—Types of Neural Networks, Image Segmentation. I Artificial Intelligence in Dentistry (s. 283-316). Springer. https://doi.org/10.1007/978-3-031-43827-1_17

Pauwels, R. & Iosifidis, A. (2024). Deep Learning in Image Processing: Part 2—Image Enhancement, Reconstruction and Registration. I K. Orhan & R. Jagtap (red.), Artificial Intelligence in Dentistry (s. 317-351). Springer. https://doi.org/10.1007/978-3-031-43827-1_18

Cao, G., Iosifidis, A., Gabbouj, M., Raghavan, V. & Gottumukkala, R. (2021). Deep Multi-view Learning to Rank. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1426-1438. Artikel 8845659. https://doi.org/10.1109/TKDE.2019.2942590

Passalis, N., Tefas, A., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2019). Deep Temporal Logistic Bag-of-features for Forecasting High Frequency Limit Order Book Time Series. I 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (s. 7545-7549). Artikel 8682297 IEEE. https://doi.org/10.1109/ICASSP.2019.8682297

Dong, Z., Li, N., Iosifidis, A. & Zhang, Q. (2022). Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing. ArXiv. https://doi.org/10.48550/arXiv.2211.13778

Leporowski, B. T., Tola, D., Hansen, C. & Iosifidis, A. (2022). Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving. I A.-L. Andersen, R. Andersen, T. D. Brunoe, M. Stoettrup Schioenning Larsen, K. Nielsen, A. Napoleone & S. Kjeldgaard (red.), Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems - Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference CARV 2021 and 10th World Mass Customization and Personalization Conference MCPC 2021 (s. 224-232). Springer. https://doi.org/10.1007/978-3-030-90700-6_25

Iosifidis, A., Tefas, A. & Pitas, I. (2012). Discriminant Action Representation for View-Invariant Person Identification. I Image Processing (ICIP), 2012 19th IEEE International Conference on (s. 1177-1180). IEEE. https://doi.org/10.1109/ICIP.2012.6467075

Iosifidis, A., Tefas, A. & Pitas, I. (2014). Discriminant Bag of Words based Representation for Human Action Recognition. Pattern Recognition Letters, 49, 185-192. https://doi.org/10.1016/j.patrec.2014.07.011

Iosifidis, A., Tefas, A. & Pitas, I. (2015). Distance-based Human Action Recognition using optimized class representations. Neurocomputing, 161, 47-55. https://doi.org/10.1016/j.neucom.2014.10.088

Li, N., Iosifidis, A. & Zhang, Q. (2022). Distributed Deep Learning Inference Acceleration using Seamless Collaboration in Edge Computing. I ICC 2022 - IEEE International Conference on Communications (s. 3667-3672). IEEE. https://doi.org/10.1109/ICC45855.2022.9839083

Iosifidis, A., Tefas, A. & Pitas, I. (2015). DropELM: Fast Neural Network Regularization with Dropout and DropConnect. Neurocomputing, 162, 57-66. https://doi.org/10.1016/j.neucom.2015.04.006

Iosifidis, A., Tefas, A. & Pitas, I. (2013). Dynamic Action Classification based on Iterative Data Selection and Feedforward Neural Networks. I Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 21st European (s. 1-5). IEEE. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6811572

Iosifidis, A., Tefas, A. & Pitas, I. (2012). Dynamic action recognition based on Dynemes and Extreme Learning Machine. Pattern Recognition Letters, 34(15), 1890-1898. https://doi.org/10.1016/j.patrec.2012.10.019

Bakhtiarnia, A., Milošević, N., Zhang, Q., Bajović, D. & Iosifidis, A. (2023). Dynamic Split Computing for Efficient Deep Edge Intelligence. I ICASSP 2023: 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) IEEE. https://doi.org/10.1109/ICASSP49357.2023.10096914

Iosifidis, A., Marami, E., Tefas, A. & Pitas, I. (2012). Eating and Drinking activity recognition based on Discriminant Analysis of fuzzy distances and Activity Volumes. I Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on (s. 2201-2204). IEEE. https://doi.org/10.1109/ICASSP.2012.6288350

Hvilshøj, F., Iosifidis, A. & Assent, I. (2021). ECINN: Efficient Counterfactuals from Invertible Neural Networks. I 32nd British Machine Vision Conference 2021, BMVC 2021, Virtual https://arxiv.org/abs/2103.13701

Wan, S., Chen, C. & Iosifidis, A. (2022). Editorial to special issue on cross-media learning for visual question answering. Image and Vision Computing, 118, Artikel 104355. https://doi.org/10.1016/j.imavis.2021.104355

Bakhtiarnia, A., Zhang, Q. & Iosifidis, A. (2024). Efficient High-Resolution Deep Learning: A Survey. ACM Computing Surveys, 56(7), 1 - 35. Artikel 181. https://doi.org/10.1145/3645107

Sohrab, F., Raitoharju, J., Iosifidis, A. & Gabbouj, M. (2020). Ellipsoidal Subspace Support Vector Data Description. IEEE Access, 8, 122013-122025. Artikel 9133428. https://doi.org/10.1109/ACCESS.2020.3007123

Iosifidis, A., Tefas, A. & Pitas, I. (2015). Enhancing Class Discrimination in Kernel Discriminant Analysis. I Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on (s. 1926-1930). IEEE. https://doi.org/10.1109/ICASSP.2015.7178306

Iosifidis, A., Tefas, A. & Pitas, I. (2015). Enhancing ELM-based facial image classification by exploiting multiple facial views. I Procedia Computer Science (Bind 51, s. 2814-2821). ELSEVIER. https://doi.org/10.1016/j.procs.2015.05.440

Kiranyaz, S., Malik, J., Abdallah, H. B., Ince, T., Iosifidis, A. & Gabbouj, M. (2021). Exploiting heterogeneity in operational neural networks by synaptic plasticity. Neural Computing and Applications, 33(13), 7997-8015. https://doi.org/10.1007/s00521-020-05543-w

Iosifidis, A., Tefas, A. & Pitas, I. (2014). Exploiting Local Class Information in Extreme Learning Machine. I IEEE Symposium Series on Computational Intelligence (SSCI): Computational Intelligence for Engineering Solutions http://poseidon.csd.auth.gr/papers/PUBLISHED/CONFERENCE/pdf/2014/Iosifidis_2014_NCTA.pdf

Mademlis, I., Iosifidis, A., Tefas, A., Nikolaidis, N. & Pitas, I. (2015). Exploiting Stereoscopic Disparity for Augmenting Human Activity Recognition Performance. Multimedia Tools and Applications, 75(19), 11641-11660. https://doi.org/10.1007/s11042-015-2719-x

Mygdalis, V., Iosifidis, A., Tefas, A. & Pitas, I. (2015). Exploiting subclass information in one-class support vector machine for video summarization. I Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on (s. 2259-2263). IEEE. https://doi.org/10.1109/ICASSP.2015.7178373

Iosifidis, A. (2015). Extreme Learning Machine based Supervised Subspace Learning. Neurocomputing, 167, 158-164. https://doi.org/10.1016/j.neucom.2015.04.083

Tsapanos, N., Tefas, A., Nikolaidis, N., Iosifidis, A. & Pitas, I. (2015). Fast Kernel Matrix Computation for Big Data Clustering. I Procedia Computer Science (s. 2445-2452). ELSEVIER. https://doi.org/10.1016/j.procs.2015.05.352

Ntakaris, A., Mirone, G., Kanniainen, J., Gabbouj, M. & Iosifidis, A. (2019). Feature Engineering for Mid-Price Prediction with Deep Learning. IEEE Access, 7, 82390 - 82412. Artikel 8743410. https://doi.org/10.1109/ACCESS.2019.2924353

Chumachenko, K., Iosifidis, A. & Gabbouj, M. (2022). Feedforward Neural Networks Initialization based on Discriminant Learning. Neural Networks, 146, 220-229. https://doi.org/10.1016/j.neunet.2021.11.020

Jensen, R. I. T. & Iosifidis, A. (2023). Fighting Money Laundering With Statistics and Machine Learning. IEEE Access, 11, 8889-8903. https://doi.org/10.1109/ACCESS.2023.3239549

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Alexandros Iosifidis