1 Schedule¶

Seminars_schedule

2 Requirements¶

25 minutes for one presentation.
Main requirements to presentation:

• to be prepared in LaTeX (or Jupyter Notebook with LaTeX inline),
• to be short, understandable, clear and convinient,
• no more than 20 minutes for content deliver and 5 for questions,
• references on the last slide

3 Topics¶

Each student has to present a research and part of his thesis.

Opened list of cutting-edge topics:

Topic	Link	Reporter	Scheduled
[x]MXNet DL framework	Chen T. et al. Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems //arXiv preprint arXiv:1512.01274. – 2015. URL: https://arxiv.org/pdf/1512.01274	Petr Gusev	Mar-21 Moved to Mar-28
Manifold MixUp	Manifold Mixup: Better Representations by Interpolating Hidden States. URL: https://arxiv.org/pdf/1806.05236v4	-
SphereFace	SphereFace: Deep Hypersphere Embedding for Face Recognition URL: https://arxiv.org/pdf/1704.08063.pdf	-
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]
DisCoCat model	Grefenstette E. Category-theoretic quantitative compositional distributional models of natural language semantics //arXiv preprint arXiv:1311.1539. – 2013. URL: https://arxiv.org/abs/1311.1539
[x]DisCoCat toy model	Gogioso S. A Corpus-based Toy Model for DisCoCat //arXiv preprint arXiv:1605.04013. – 2016. URL: https://arxiv.org/pdf/1605.04013.pdf	Anik Chakrabarthy	Mar-14
[x]Triplet Loss	https://arxiv.org/pdf/1503.03832.pdf	Alexandra Luchkina	Mar-7
[ ]Style transfer SotA (state-of-the-art)	A Style-Based Generator Architecture for Generative Adversarial Networks. URL: https://arxiv.org/abs/1812.04948	Klim Markelov (no LaTeX presentation)	Mar-14
[x]Pixel Recurrent Neural Networks	Oord A., Kalchbrenner N., Kavukcuoglu K. Pixel recurrent neural networks //arXiv preprint arXiv:1601.06759. – 2016. URL:https://arxiv.org/abs/1601.06759	Omid Razizadeh	Mar-14
Performance of Word Embeddings	review and experience	Andrey Zubkov	Mar-7 Replaced by another topic
[x]CosFace	Wang H. et al. Cosface: Large margin cosine loss for deep face recognition //Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. – 2018. – С. 5265-5274. URL: https://arxiv.org/pdf/1801.09414.pdf	Akilesh Sivaswamy	Mar-7
[ ]CNN for speech command recognition. Review		Roman Kozinets	Apr-4
[x]Text clustering	Xu J. et al. Self-taught convolutional neural networks for short text clustering //Neural Networks. – 2017. – Т. 88. – С. 22-31. URL: https://arxiv.org/abs/1701.00185	Evgeniy Kurochkin	Mar-7
[ ]A Quantum-Theoretic Approach to Distributional Semantics	Blacoe W., Kashefi E., Lapata M. A quantum-theoretic approach to distributional semantics //Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. – 2013. – С. 847-857. URL: http://www.aclweb.org/anthology/N13-1105	Ivan Rogalsky	Apr-4
[ ]State of the art Deep Learning - Intelligent Network Traffic Control Systems	State of the art Deep Learning: Evolving Machine Intelligence Toward Tomorrow's Intelligent Network Traffic Control Systems. URL: https://ieeexplore.ieee.org/document/7932863	Ravi Kumar	Mar-28 Moved to Apr-04
Solving the Quantum Many-Body problem with ANN	Carleo G., Troyer M. Solving the quantum many-body problem with artificial neural networks //Science. – 2017. – Т. 355. – №. 6325. – С. 602-606. URL: https://arxiv.org/pdf/1606.02318	-	-
[ ]Predicting Oil Movement in a development System Using Deep Latent Dynamic Models	URL: Video: https://www.youtube.com/watch?v=N3iV-F4aqLA? Slieds: https://bayesgroup.github.io/bmml_sem/2018/Temirchev_Metamodelling.pdf	Jetina Tsvaki	Apr-18
[x]Artificial Intelligence Safety and Cybersecurity: a Timeline of AI Failures	Yampolskiy R. V., Spellchecker M. S. Artificial Intelligence Safety and Cybersecurity: A Timeline of AI Failures (2016) //arXiv preprint arXiv:1610.07997. URL: https://arxiv.org/abs/1610.07997	Thomas Vialars	Mar-28
Understanding consumer behavior	Lang T., Rettenmeier M. Understanding consumer behavior with recurrent neural networks //Workshop on Machine Learning Methods for Recommender Systems. – 2017. URL: https://doogkong.github.io/2017/papers/paper2.pdf	Seth Gyamerah	Mar-21 ?
[ ]Using ML for Network Intrusion Detection	Sommer R., Paxson V. Outside the closed world: On using machine learning for network intrusion detection //2010 IEEE symposium on security and privacy. – IEEE, 2010. – С. 305-316. URL: https://www.computer.org/csdl/proceedings/sp/2010/6894/00/05504793.pdf	Dylan Bersans	Apr-18
[ ]Tacotron 2	Shen J. et al. Natural tts synthesis by conditioning wavenet on mel spectrogram predictions //2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). – IEEE, 2018. – С. 4779-4783. URL: https://arxiv.org/pdf/1712.05884.pdf	Leyuan Sheng	Apr-11
BERT (Google)	Devlin J. et al. Bert: Pre-training of deep bidirectional transformers for language understanding //arXiv preprint arXiv:1810.04805. – 2018. URL: https://arxiv.org/abs/1810.04805
[x]Universal Sentence Encoder	Cer D. et al. Universal sentence encoder //arXiv preprint arXiv:1803.11175. – 2018. URL: https://arxiv.org/pdf/1803.11175.pdf	Elizaveta Tagirova	Feb-28
[x]ULMFiT	Howard J., Ruder S. Universal language model fine-tuning for text classification //arXiv preprint arXiv:1801.06146. – 2018. URL: https://arxiv.org/pdf/1801.06146.pdf	Juan Pinzon	Mar-21
ELMo	Peters M. E. et al. Deep contextualized word representations //arXiv preprint arXiv:1802.05365. – 2018. URL: http://www.aclweb.org/anthology/N18-1202
[ ]Skip-thoughts, Infersent, RandSent - Facebook	1. Kiros R. et al. Skip-thought vectors //Advances in neural information processing systems. – 2015. – С. 3294-3302. URL: https://arxiv.org/pdf/1506.06726.pdf 2. Conneau A. et al. Supervised learning of universal sentence representations from natural language inference data //arXiv preprint arXiv:1705.02364. – 2017. URL: https://arxiv.org/abs/1705.02364 3. Wieting J., Kiela D. No Training Required: Exploring Random Encoders for Sentence Classification //arXiv preprint arXiv:1901.10444. – 2019. URL: https://arxiv.org/pdf/1901.10444.pdf	Madina Tussupova (3)	Apr-4
[ ]BigARTM	Vorontsov K. et al. Bigartm: Open source library for regularized multimodal topic modeling of large collections //International Conference on Analysis of Images, Social Networks and Texts. – Springer, Cham, 2015. – С. 370-381. URL: http://www.machinelearning.ru/wiki/images/e/ea/Voron15aist.pdf	Olga Yakovenko
[x]Reinforcement Learning	Human-level control through deep reinforcement learning	Munyaradi Njera	Mar-28
	Selsam, D., Lamm, M., Bünz, B., Liang, P., de Moura, L., & Dill, D. L. (2018). Learning a SAT solver from single-bit supervision. arXiv preprint arXiv:1802.03685.	Andrey Marinov	May-23

4 Topics of master thesis¶

Opened list of reports on master thesis (statement of work, review, and results):

Reporter	Topic	Scheduled
1st year students
1 Chakrabarti Anik		May-16
2 Razizadeh Omid		May-16
3 Siyoto Owen
4 Munyaradzi Njera		May-23
5 Averyanov Evgeniy
6 Kozinets Roman		May-16
7 Melnikov Arsentiy
8 Rogalsky Ivan	Open System Categorical Quantum Semantics in NLP (master thesis, review)	Apr-18
9 Urynbassarov Mukhtar
10 Yakovenko Olga
[x]11 Tagirova Elizaveta		Mar-21
12 Tsvaki Jetina		May-16
13 Ravi Kumar		Apr-18
2st year students
1 Leyuan Sheng		Apr-25
2 Akilesh Sivaswamy		Apr-4
3 Juan Fernando Pinzon Correa		Apr-11
4 Gyamerah Seth
5 Fishman Daniil
[x]6 Gusev Petr		Mar-28
7 Kurochkin Evgeniy		Apr-25
8 Luchkina Anastasia		Apr-25
9 Malysheva Anastasia
10 Sergeev Artem
11 Zubkov Andrey		Apr-25
12 Poteshkin Vitaly
13 Markelov Klim		Apr-11
14 Tussupova Madina		Apr-4
15 Marinov Andrey		May-16

5 At fault¶

These students still didn't selected a paper to report or doesn't assigned to a time slot:

1st year students

• Owen Siyoto: refer, master
• Evgeniy Averyanov: refer, master
• Melnikov Arsentiy: refer, master
• Mukhtar Urynbassarov: refer, master
• Olga Yakovenko: master

2nd year students

• Fishman Daniil: refer, master
• Poteshkin Vitaly: refer, master
• Gyamerah Seth: master

6 Presence¶

The requirements of the seminar are:

• AS.BDA.RQ.1) deliver presentation: (i) on the topic of master thesis, (ii) review of a recognized paper.
• AS.BDA.RQ.2) attend not less than 50% of classes.

Here is a table of Presence conducted from meeting minutes (see minutes as issues in first clumn of the Seminars schedule.