Scientific workshop "Big Data Analytics"

25 minutes for one presentation.
Each student has to present a research.

Opened list of cutting-edge topics:
|_.Topic|_.Link|_.Reporter|
|ResNet, ResNeXt| K. He, X. Zhang, S. Ren, and J. Sun. Deep Residual Learning for Image Recognition. In CVPR, 2016|*Petr Gusev*|
|Dynamic Word Embeddings|https://arxiv.org/abs/1804.07983| *Andrey Zubkov*|
| Capsule NN|Paper: https://arxiv.org/pdf/1710.09829, presentation: https://cedar.buffalo.edu/~srihari/CSE676/9.12%20CapsuleNets.pdf|*Klim Markelov*|
| MixUp | https://arxiv.org/pdf/1710.09412.pdf, ("AdaMixUp":https://arxiv.org/pdf/1809.02499.pdf in addition)| *Vitaly Poteshkin* |
| Variational Autoencoders || *Anastasia Malysheva*|
| ArcFace |||
| UMAP | McInnes, Leland and John Healy (2018). “UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction”. In: ArXiv e-prints. arXiv: "1802.03426 [stat.ML]":http://arxiv.org/abs/1802.03426||
| Siamese NN|||
| Quantum Semantics|||
| MobileNN|||
| Triplet Loss|||
| Sobolev Training | https://arxiv.org/pdf/1706.04859||
|Rapids AI |http://rapids.ai/ ||
|Attentioned based LSTM|https://www.aclweb.org/anthology/D16-1058|**Munyaradzi Njera**|