1 Schedule¶

Seminars_schedule

2 Requirements¶

25 minutes for one presentation.
Main requirements to presentation:

• to be prepared in LaTeX (try use https://overleaf.com),
• to be short, understandable, clear and convenient,
• 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
[ ]Zero and Few shot learning	Schonfeld, E., Ebrahimi, S., Sinha, S., Darrell, T., & Akata, Z. (2019). Generalized zero-and few-shot learning via aligned variational autoencoders. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 8247-8255). https://arxiv.org/pdf/1812.01784.pdf	Nikita Nikolaev	20-Feb
[ ] ERNIE	Zhang, Z., Han, X., Liu, Z., Jiang, X., Sun, M., & Liu, Q. (2019). ERNIE: Enhanced language representation with informative entities. arXiv preprint arXiv:1905.07129.	Daria Pirozhkova	27-Feb
[ ] Reservoir Computing	Design Strategies for Weight Matrices of Echo State Networks	Sergey Garmaev	27-Feb
[ ] Hybrid VAE for NLG	Semeniuta, S., Severyn, A., & Barth, E. (2017). A hybrid convolutional variational autoencoder for text generation. arXiv preprint arXiv:1702.02390.	Elena Voskoboy	5-Mar
[ ] NASNet and AutoML	AutoML for large scale image classification and object detection, http://arxiv.org/abs/1707.07012	Oladotun Aluko	12-Mar
[ ]BERT-DST	Chao, G. L., & Lane, I. (2019). Bert-dst: Scalable end-to-end dialogue state tracking with bidirectional encoder representations from transformer. arXiv preprint arXiv:1907.03040.	Alexey Korolev	16-Apr
[ ]	Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8), 9. https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf	Alexander Rusnak	02-Apr
[ ]Variational Circuits	Chen, S. Y. C., & Goan, H. S. (2019). Variational Quantum Circuits and Deep Reinforcement Learning. arXiv preprint arXiv:1907.00397.	Kirill Kalmutskiy	12-Mar
[ ]	https://pennylane.ai/qml/zreferences.html#huggins2018towards Kosuke Mitarai, Makoto Negoro, Masahiro Kitagawa, and Keisuke Fujii. Quantum circuit learning. 2018. arXiv:1803.00745.	Andrey Yashkin	5-Mar
[ ]Reynolds Averaged Turbulence Modeling using Deep Neural Networks with Embedded Invariance.	Julia Ling and Jeremy Templeton.Sandia National Laboratories. https://www.cambridge.org/core/journals/journal-of-fluid-mechanics/article/reynolds-averaged-turbulence-modelling-using-deep-neural-networks-with-embedded-invariance/0B280EEE89C74A7BF651C422F8FBD1EB	Omid Razizadeh	26-Mar
[ ] Tensor Networks in QML	William Huggins, Piyush Patel, K Birgitta Whaley, and E Miles Stoudenmire. Towards quantum machine learning with tensor networks. 2018. arXiv:1803.11537.	Raphael Blankson	30-Apr
[ ] Tensor Networks	Edwin Stoudenmire and David J Schwab. Supervised learning with tensor networks. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems 29, 4799–4807. Curran Associates, Inc., 2016. URL: http://papers.nips.cc/paper/6211-supervised-learning-with-tensor-networks.pdf.	Richard Fambon
[ ] PennyLane.ai	Overview of the framework	Raphael Blankson as master topic	28-May
[ ] Jasper	Li, J., Lavrukhin, V., Ginsburg, B., Leary, R., Kuchaiev, O., Cohen, J. M., ... & Gadde, R. T. (2019). Jasper: An end-to-end convolutional neural acoustic model. arXiv preprint arXiv:1904.03288.	Geoffroy de Felcourt	19-Mar
[ ] Weight Uncertainty	Blundell, C., Cornebise, J., Kavukcuoglu, K., & Wierstra, D. (2015). Weight uncertainty in neural networks. arXiv preprint arXiv:1505.05424.	Antoine Logeais	9-Apr
[ ]	Wen, Y., Vicol, P., Ba, J., Tran, D., & Grosse, R. (2018). Flipout: Efficient pseudo-independent weight perturbations on mini-batches. arXiv preprint arXiv:1803.04386.	Thibault Kollen	2-Apr
[ ]	Lample, G., & Charton, F. (2019). Deep learning for symbolic mathematics. arXiv preprint arXiv:1912.01412. Facebook	Mikhail Liz	5-Mar
[ ] Layer-wise relevance propagation	Montavon, G., Binder, A., Lapuschkin, S., Samek, W., & Müller, K. R. (2019). Layer-wise relevance propagation: an overview. In Explainable AI: Interpreting, Explaining and Visualizing Deep Learning (pp. 193-209). Springer, Cham.	Rohan Rathore	9-Apr
[ ] Deep Reinforcement Learning		Kaivalya Pandey	26-Mar
[ ] Complex Conv	Complex Convolution. IEEE	Ravi Kumar	30-Mar
[ ]	Understanding mixup training methods https://ieeexplore.ieee.org/document/8478159	Jetina Tsvaki	16-Apr
[ ]	Deep Learning based Approach to Reduced Order Modelling... https://arxiv.org/abs/1804.09269	Alix Bernard	26-Mar
[ ] Deep learning: A Generic approach for extreme condition traffic		Watana Pongsapas	12-Mar
[ ]	Uzkent B, Sheehan E, Meng C, Tang Z, Burke M, Lobell D, Ermon S. Learning to interpret satellite images in global scale using wikipedia. arXiv preprint arXiv:1905.02506. 2019 May 7.	Owen Siyoto	9-Apr
[ ] Implicit weight uncertainty in neural networks	Nick Pawlowski, etc	Alexander Donets	16-Apr
[ ] Brain Tumor	Brain Tumor Sementation Using Deep Learning by Type Specific Sorting of Images	Dinesh Reddy	19-Mar
[ ] Blockchain for AI	Salah, K., Rehman, M. H. U., Nizamuddin, N., & Al-Fuqaha, A. (2019). Blockchain for AI: Review and open research challenges. IEEE Access, 7, 10127-10149.	Abhishek Saxena	9-Apr
From previous semester (not reported yet)
[ ]Manifold MixUp	Manifold Mixup: Better Representations by Interpolating Hidden States. URL: https://arxiv.org/pdf/1806.05236v4
Quantum
[ ]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
[ ]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	Thibault Kollen	04-June
[ ]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
Speech
[ ]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	Geoffroy de felcourt	04-June
Natural Language Processing
[ ]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	Antoine Logeais	04-June
[ ]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	Richard Fambon	04-June
[ ]	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.

Papers with code
[ ]Deep-speare	Deep-speare: A Joint Neural Model of Poetic Language, Meter and Rhyme https://paperswithcode.com/paper/deep-speare-a-joint-neural-model-of-poetic	Elizaveta Tagirova	19-Dec

4 Topics of master thesis¶

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

Reporter	Topic	Scheduled
1st year students, review, method
1 to-be-listed		-
1 Raphael Blankson	PennyLane.ai overview	28-May
2 Kirill Kalmutskiy	Coursework	30-Apr
2st year students, results
1 Razizadeh Omid
2 Siyoto Owen
3 Munyaradzi Njera
4 Kozinets Roman
5 Tagirova Elizaveta
6 Tsvaki Jetina
7 Ravi Kumar

5 At fault¶

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

1st year students

• noname: refer, master

2nd year students

• noname: refer, 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 column of the Seminars schedule.