Seminars schedule » History » Version 90

Seminar "Big Data Analytics"¶

1. 2nd year students coursework pre-defence: (1) #2008, (2) 14.09.2018.

18, #2028	Planning the semester
25, #	Klim Markelov, Capsule NN Andrey Zubkov, Dynamic Word Embeddings https://arxiv.org/abs/1804.07983 Vitaly Poteshkin, MixUp Planning the semester

1, #	Petr Gusev. ResNet, ResNeXt K. He, X. Zhang, S. Ren, and J. Sun. Deep Residual Learning for Image Recognition. In CVPR, 2016 Anastasia Malysheva, Variational Autoencoders Anik Chakrabarthy. Quantum Semantics ~_{25 min slot}~
8, #	Akilesh Sivawamy, Mobile NN. Lee Wonjai. Bayesian CDF Polina Potapova, XNOR-Net, https://arxiv.org/pdf/1603.05279.pdf (moved to December) Olga Yakovenko, RNN for Speech Recognition
15, #	Ravi Kumar, Deep Learning in Mobile and Wireless Networks: a Survey Munjaradzi Njera, Attentioned based LSTM Juan Fernando Pinzon Correa, Rapids AI Urynbassarov Mukhtar, GAN
22, #	lost
29, #	Roman Kozinets, Siamese NN Omid Razizadeh, tSNE Jetina Tsvaki, Natural language based financial forecasting: a survey Owen Siyoto, Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network

6, #2034	Leyuan Sheng, CycleGAN Tagirova Elizaveta. LIME (local interpretable model-agnostic explanations) https://arxiv.org/pdf/1602.04938v1.pdf Luchkina Alexandra, Sobolev Training Fishman Daniil, Prediction of enhancer-promoter interactions via natural language processing
13, #	Anton Kolonin. Topics for master work. Evgeniy Averyanov, Schmidt, Mark. Minimizing finite sums with the stochastic average gradient / M. Schmidt, N. Le Roux, F. Bach // Mathematical Programming. — 2017. — Vol. 162(1). — P. 83–112. — URL: https://link.springer.com/article/10.1007/s10107-016-1030-6 Polina Potapova, XNOR-Net, https://arxiv.org/pdf/1603.05279.pdf Lee Wonjai. Bayesian CDF (continue with example)
20, #	Artem Sergeev. ArcFace Gyamerah Seth, Fuzzy time series on financial forecasting Mulley Loic, Triplet Loss https://arxiv.org/pdf/1503.03832.pdf Kurochkin Evgeniy, DenseNet
27, #	Julien Machet, Machine learning top down and bottom up. Ivan Rogalsky Arsentiy Melnikov Loic Mulley Artem Sergeev