Iryna Korshunova
Iryna Korshunova
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Cited by
Cited by
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
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
Faster gaze prediction with dense networks and fisher pruning
L Theis, I Korshunova, A Tejani, F Huszár
arXiv preprint arXiv:1801.05787, 2018
Music transcription modelling and composition using deep learning
BL Sturm, JF Santos, O Ben-Tal, I Korshunova
arXiv preprint arXiv:1604.08723, 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
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
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
Classifying plankton with deep neural networks
S Dieleman, A Van den Oord, I Korshunova, J Burms, J Degrave, L Pigou, ...
Blog entry, 2015
Discriminative topic modeling with logistic LDA
I Korshunova, H Xiong, M Fedoryszak, L Theis
Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 2019
Fast face-morphing using neural networks
L Theis, I Korshunova, W Shi, Z Wang
US Patent 10,552,977, 2020
Conditional bruno: A neural process for exchangeable labelled data
I Korshunova, Y Gal, A Gretton, J Dambre
Neurocomputing 416, 305-309, 2020
Diagnosing heart diseases with deep neural networks
I Korshunova
Blog entry, 2016
Epileptic seizure prediction using deep learning
I Korshunova, O Thas
Universiteit Gent. Belgium, 2015
Music transcription modelling and composition using deep learning
BL Sturm, JF Santos, O Ben-Tal, I Korshunova
arXiv preprint arXiv:1604.08723, 0
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
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
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
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
BRUNO: Exchangeable Deep Learning Models of Predictive Distributions
I Korshunova
University College London, United Kingdom, 2020
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