Stanislau Semeniuta
Stanislau Semeniuta
Samsung AI Center Moscow
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Cited by
Cited by
A hybrid convolutional variational autoencoder for text generation
S Semeniuta, A Severyn, E Barth
arXiv preprint arXiv:1702.02390, 2017
Recurrent dropout without memory loss
S Semeniuta, A Severyn, E Barth
arXiv preprint arXiv:1603.05118, 2016
On accurate evaluation of gans for language generation
S Semeniuta, A Severyn, S Gelly
arXiv preprint arXiv:1806.04936, 2018
Robust real-time extreme head pose estimation
S Tulyakov, RL Vieriu, S Semeniuta, N Sebe
2014 22nd International Conference on Pattern Recognition, 2263-2268, 2014
Facial expression recognition under a wide range of head poses
RL Vieriu, S Tulyakov, S Semeniuta, E Sangineto, N Sebe
2015 11th IEEE International Conference and Workshops on Automatic Face and …, 2015
TN-Grid and gene@ home project: Volunteer Computing for Bioinformatics
F Asnicar, N Sella, L Masera, P Morettin, T Tolio, S Semeniuta, C Moser, ...
BOINC: FAST 2015 International Conference BOINC: FAST 2015Second …, 2015
NES2RA: Network expansion by stratified variable subsetting and ranking aggregation
F Asnicar, L Masera, E Coller, C Gallo, N Sella, T Tolio, P Morettin, ...
The International Journal of High Performance Computing Applications 32 (3 …, 2018
Discovering candidates for gene network expansion by distributed volunteer computing
F Asnicar, L Erculiani, F Galante, C Gallo, L Masera, P Morettin, N Sella, ...
2015 IEEE Trustcom/BigDataSE/ISPA 3, 248-253, 2015
A Hybrid Convolutional Variational Autoencoder for Text Generation. arXiv
S Semeniuta, A Severyn, E Barth
arXiv preprint arXiv:1702.02390, 2017
Image classification with recurrent attention models
S Semeniuta, E Barth
2016 IEEE Symposium Series on Computational Intelligence (SSCI), 1-7, 2016
Recurrent Neural Networks for Discriminative and Generative Learning
S Semeniuta
Universität zu Lübeck, 2019
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