Meeting #2028
Updated by Evgeniy Pavlovskiy almost 7 years ago
25 minutes for one presentation.
Each student has to present a research.
Opened list of cutting-edge topics:
|_.Topic|_.Link|_.Reporter|_.Scheduled|
|ResNet, ResNeXt| K. He, X. Zhang, S. Ren, and J. Sun. Deep Residual Learning for Image Recognition. In CVPR, 2016|*Petr Gusev*|1-nov|
|Dynamic Word Embeddings|https://arxiv.org/abs/1804.07983| *Andrey Zubkov*|25-oct|
| Capsule NN|Paper: https://arxiv.org/pdf/1710.09829, presentation: https://cedar.buffalo.edu/~srihari/CSE676/9.12%20CapsuleNets.pdf|*Klim Markelov*|25-oct|
| MixUp | https://arxiv.org/pdf/1710.09412.pdf, ("AdaMixUp":https://arxiv.org/pdf/1809.02499.pdf in addition)| *Vitaly Poteshkin* |25-oct|
| Variational Autoencoders || *Anastasia Malysheva*|1-nov|
| ArcFace ||*Sergeyev Artem*|-|
| 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|*Burgeot Guillaume*|-|
| Siamese NN||*Roman Kozinets*|29-nov|
| Quantum Semantics||*Chakrabathy Anik*|1-nov|
| MobileNN||*Akilesh Sivaswamy*|8-nov|
| Triplet Loss||*Mulley Loïc*|-|
| Sobolev Training | https://arxiv.org/pdf/1706.04859|*Luchkina Anastasia*|3-dec| https://arxiv.org/pdf/1706.04859||
|Rapids AI |http://rapids.ai/ |*Juan Fernando Pinzon Correa*|15-nov|
|Attentioned based LSTM|https://www.aclweb.org/anthology/D16-1058|**Munyaradzi Njera**|15-nov|
|CycleGAN||*Leyuan Sheng*|3-dec|
|RNN for Speech Recognition|? https://www.cs.toronto.edu/~graves/icassp_2013.pdf|*Yakovenko Olga*|-|
|Bayesian Conditional Density Filtering|? https://arxiv.org/abs/1401.3632|*Lee Wonjae*|8-nov|
|Deep Learning in Mobile and Wireless Networks: a Survey|https://arxiv.org/pdf/1803.04311.pdf|*Ravi Kumar*|15-nov|
|tSNE|Maaten L., Hinton G. Visualizing data using t-SNE //Journal of machine learning research. – 2008. – Т. 9. – №. Nov. – С. 2579-2605. http://www.jmlr.org/papers/v9/vandermaaten08a.html|*Razizadeh Omid*|29-nov|
|Natural language based financial forecasting: a survey|Xing F. Z., Cambria E., Welsch R. E. Natural language based financial forecasting: a survey //Artificial Intelligence Review. – 2018. – Т. 50. – №. 1. – С. 49-73.|*Tsvaki Jetina*|29-nov|
|Super-Resolution|Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network http://openaccess.thecvf.com/content_cvpr_2017/papers/Ledig_Photo-Realistic_Single_Image_CVPR_2017_paper.pdf| *Siyoto Owen*|29-nov|
|LIME (local interpretable model-agnostic explanations)|Ribeiro M. T., Singh S., Guestrin C. Why should i trust you?: Explaining the predictions of any classifier //Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. – ACM, 2016. – С. 1135-1144. https://arxiv.org/pdf/1602.04938v1.pdf|*Tagirova Elizaveta*|3-dec|
|XNOR-Net|https://arxiv.org/pdf/1603.05279.pdf|*Potapova Polina*|8-nov|
These students still didn't selected a paper to report:
1st year students
# *Averyanov Evgeniy*
# *Melnikov Arsentiy*
# *Rogalsky Ivan*
# *Urynbassarov Mukhtar*
# *Machet Julien*
2nd year students
# *Gyamerah Seth*
# *Fishman Daniil*
# *Kurochkin Evgeniy*
# *Luchkina Anastasia*