Meeting #2054
Updated by Evgeniy Pavlovskiy over 6 years ago
h1. 1 Schedule [[Seminars_schedule]] h1. 2 Requirements 25 minutes for one presentation. Main requirements to presentation: * to be prepared in LaTeX, * to be short, understandable, clear and convinient, * no more than 20 minutes for content deliver and 5 for questions, * references on the last slide h1. 3 Topics Each student has to present a research and part of his thesis. Opened list of cutting-edge topics: |_.Topic|_.Link|_.Reporter|_.Scheduled| |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|?|?| |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]":http://arxiv.org/abs/1802.03426||| | 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|*Elizaveta Tagirova*|Feb-28| |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| |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* | Mar-14| |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| |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| |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| |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|*Munjaradzi Njera*|Mar-28| |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| |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*|?| |Review on master topic||*Seth Gyamerah*|?| |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||| |BERT|||| |Universal Sentence Encoder|Cer D. et al. Universal sentence encoder //arXiv preprint arXiv:1803.11175. – 2018. URL: https://arxiv.org/pdf/1803.11175.pdf||| Encoder|||| |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||| |ULMfit|||| |ELMo|Peters M. E. et al. Deep contextualized word representations //arXiv preprint arXiv:1802.05365. – 2018. URL: http://www.aclweb.org/anthology/N18-1202||| |ELMo|||| |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||| RandSent|||| h1. 4 Topics of master thesis Opened list of reports on master thesis (statement of work, review, and results): |_.Reporter|_.Topic|_.Scheduled| |\3=.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 | || |11 Tagirova Elizaveta | || |12 Tsvaki Jetina | |May-16| |13 Ravi Kumar | |Apr-18| |\3=.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 | | | |6 Gusev Petr | | | |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 | | | |15 Marinov Andrey | | | h1. 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*: refer, master * *Elizaveta Tagirova*: master * *Thomas Vialars*: refer - time slot 2nd year students * *Fishman Daniil*: refer, master * *Gusev Petr*: refer, master * *Malysheva Anastasia*: refer, master * *Poteshkin Vitaly*: refer, master * *Tussupova Madina*: refer, master * *Marinov Andrey*: refer, master * *Gyamerah Seth*: refer - timeslot, master