| Fast face-swap using convolutional neural networks I Korshunova, W Shi, J Dambre, L Theis Proceedings of the IEEE international conference on computer vision, 3677-3685, 2017 | 507 | 2017 |
| Crowdsourcing reproducible seizure forecasting in human and canine epilepsy BH Brinkmann, J Wagenaar, D Abbot, P Adkins, SC Bosshard, M Chen, ... Brain 139 (6), 1713-1722, 2016 | 262 | 2016 |
| Faster gaze prediction with dense networks and fisher pruning L Theis, I Korshunova, A Tejani, F Huszár arXiv preprint arXiv:1801.05787, 2018 | 235 | 2018 |
| Music transcription modelling and composition using deep learning BL Sturm, JF Santos, O Ben-Tal, I Korshunova arXiv preprint arXiv:1604.08723, 2016 | 232 | 2016 |
| Folk music style modelling by recurrent neural networks with long short term memory units B Sturm, JF Santos, I Korshunova 16th international society for music information retrieval conference, 2015 | 59 | 2015 |
| Towards improved design and evaluation of epileptic seizure predictors I Korshunova, PJ Kindermans, J Degrave, T Verhoeven, BH Brinkmann, ... IEEE Transactions on Biomedical Engineering 65 (3), 502-510, 2017 | 52 | 2017 |
| Bruno: A deep recurrent model for exchangeable data I Korshunova, J Degrave, F Huszár, Y Gal, A Gretton, J Dambre Advances in Neural Information Processing Systems 31 (NeurIPS 2018), 2018 | 38 | 2018 |
| Classifying plankton with deep neural networks S Dieleman, A Van den Oord, I Korshunova, J Burms, J Degrave, L Pigou, ... Blog entry, 2015 | 26* | 2015 |
| Fast face-morphing using neural networks L Theis, I Korshunova, W Shi, Z Wang US Patent 10,552,977, 2020 | 22 | 2020 |
| Discriminative topic modeling with logistic LDA I Korshunova, H Xiong, M Fedoryszak, L Theis Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 2019 | 22 | 2019 |
| Conditional bruno: A neural process for exchangeable labelled data I Korshunova, Y Gal, A Gretton, J Dambre Neurocomputing 416, 305-309, 2020 | 14* | 2020 |
| Diagnosing heart diseases with deep neural networks I Korshunova Blog entry, 2016 | 7 | 2016 |
| Epileptic seizure prediction using deep learning I Korshunova, O Thas Universiteit Gent. Belgium, 2015 | 7 | 2015 |
| Music transcription modelling and composition using deep learning BL Sturm, JF Santos, O Ben-Tal, I Korshunova arXiv preprint arXiv:1604.08723, 0 | 6 | |
| A closer look at the adversarial robustness of information bottleneck models I Korshunova, D Stutz, AA Alemi, O Wiles, S Gowal arXiv preprint arXiv:2107.05712, 2021 | 4 | 2021 |
| Using deep learning to estimate systolic and diastolic volumes from MRI-images J Degrave, J Burms, I Korshunova, J Dambre 25th Belgian-Dutch Conference on Machine Learning (Benelearn), 2016 | 3 | 2016 |
| Exchangeable Models in Meta Reinforcement Learning I Korshunova, J Degrave, J Dambre, A Gretton, F Huszar 4th Lifelong Machine Learning Workshop at ICML 2020, 2020 | 1 | 2020 |
| Return Dispersion as an Estimator of Learning Potential for Prioritized Level Replay I Korshunova, M Jiang, J Parker-Holder, T Rocktäschel, E Grefenstette I (Still) Can't Believe It's Not Better! NeurIPS 2021 Workshop, 2021 | | 2021 |
| BRUNO: Exchangeable Deep Learning Models of Predictive Distributions I Korshunova University College London, United Kingdom, 2020 | | 2020 |
Joni DambreFull professor, Ghent UniversityVerified email at ugent.be
