Meeting #2435
Updated by Evgeniy Pavlovskiy about 5 years ago
h1. 1 Schedule
[[Seminars_schedule]]
h1. 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 convenient,
* 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|
|\4=.*General*|
|[ ]Zero-One shot Learning|Xian, Y., Lampert, C. H., Schiele, B., & Akata, Z. (2018). Zero-shot learning-a comprehensive evaluation of the good, the bad and the ugly. IEEE transactions on pattern analysis and machine intelligence. URL: https://ieeexplore.ieee.org/abstract/document/8413121 |*Vladislav Panferov*||
|[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|*Oladotun Aluko*||
|[ ] |“Why Should I Trust You?” Explaining the Predictions of Any Classifier, https://arxiv.org/pdf/1602.04938.pdf, https://github.com/marcotcr/lime|*Rohan Kumar Rathore*|?|
|[ ]Manifold MixUp| Manifold Mixup: Better Representations by Interpolating Hidden States. URL: https://arxiv.org/pdf/1806.05236v4 || |
|[ ]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|*Alix Bernard*||
|Artistic Style|Gatys L. A., Ecker A. S., Bethge M. A neural algorithm of artistic style //arXiv preprint arXiv:1508.06576. – 2015.|*Elena Voskoboy*|
|\4=.*Fluid, Oil, Physics, Chemistry*|
|[ ]Data-driven predictive using field inversion|Parish, Eric J., and Karthik Duraisamy. "A paradigm for data-driven predictive modeling using field inversion and machine learning." Journal of Computational Physics 305 (2016): 758-774.|*Omid Razizadeh*|*31-Oct*|
|[ ]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|||
|\4=.*Faces*|
|[ ]SphereFace| SphereFace: Deep Hypersphere Embedding for Face Recognition URL: https://arxiv.org/pdf/1704.08063.pdf|*Mukul Vishwas*| |
|[x]Triplet Loss|https://arxiv.org/pdf/1503.03832.pdf|*Vassily Baranov*||
|[x]Style transfer SotA (state-of-the-art)| A Style-Based Generator Architecture for Generative Adversarial Networks. URL: https://arxiv.org/abs/1812.04948 |*Michael Rodin*|5-Dec|
|Performance of Word Embeddings|review and experience|||
|[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|*Mikhail Liz*||
|\4=.*Quantum*|
|Supervised learning with quantum enhanced feature spaces|Havlíček V. et al. Supervised learning with quantum-enhanced feature spaces //Nature. – 2019. – Т. 567. – №. 7747. – С. 209. URL: https://arxiv.org/pdf/1804.11326.pdf|*Raphael Blankson*|12-Dec|
|FermiNet|Ab-Initio Solution of the Many-Electron Schr\" odinger Equation with Deep Neural Networks, https://arxiv.org/pdf/1909.02487|*Kristanek Antoine*||
|[ ]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|||
|[ ]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|||
|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|*Andrey Yashkin*||
|\4=.*Economics*|
|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|*Abhishek Saxena*, *Watana Pongsapas*||
||Li X. et al. Empirical analysis: stock market prediction via extreme learning machine //Neural Computing and Applications. – 2016. – Т. 27. – №. 1. – С. 67-78.|*Kaivalya Anand Pandey*, *Rishabh Tiwari*||
|\4=.*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|||
|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|*Nikita Nikolaev*||
|\4=.*Natural Language Processing*|
|[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 |*Alexander Donets*||
|[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|*Alexey Korolev*||
|[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|*Alexander Rusnak*||
|ELMo|Peters M. E. et al. Deep contextualized word representations //arXiv preprint arXiv:1802.05365. – 2018. URL: http://www.aclweb.org/anthology/N18-1202|*Sergey Garmaev*||
|[ ]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|||
|[ ]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|||
|[ ]Vision and Feature Norm|Vision and Feature Norms: Improving automatic feature norm learning through cross-modal maps. URL: https://aclweb.org/anthology/N16-1071|*Dinesh Reddy*|7-Nov|
|[x]Reinforcement Learning|Human-level control through deep reinforcement learning|*Kirill Kalmutskiy*||
||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.|||
|\4=.*Papers with code*|
|Weight Agnostic Neural Networks|Weight Agnostic Neural Networks, Google, https://arxiv.org/abs/1906.04358|*Roman |CBNet|CBNet A Novel composite, https://arxiv.org/abs/1909.03625|*Roman Kozinets*|*14-Nov*|
|SeqSleepNet|Phan H. et al. SeqSleepNet: end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging //IEEE Transactions on Neural Systems and Rehabilitation Engineering. – 2019. – Т. 27. – №. 3. – С. 400-410.|*Daria Pirozhkova*||
|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*|
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 to-be-listed | |-|
|\3=.2st year students|
|1 Razizadeh Omid | ||
|2 Siyoto Owen | ||
|3 Munyaradzi Njera | ||
|4 Averyanov Evgeniy | ||
|5 Kozinets Roman | |*14-Nov*|
|6 Tagirova Elizaveta | |*19-Dec*|
|7 Tsvaki Jetina | ||
|8 Ravi Kumar | ||
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
* *noname*: refer, master
2nd year students
* *noname*: refer, master
h1. 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]].
[[Seminars_schedule]]
h1. 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 convenient,
* 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|
|\4=.*General*|
|[ ]Zero-One shot Learning|Xian, Y., Lampert, C. H., Schiele, B., & Akata, Z. (2018). Zero-shot learning-a comprehensive evaluation of the good, the bad and the ugly. IEEE transactions on pattern analysis and machine intelligence. URL: https://ieeexplore.ieee.org/abstract/document/8413121 |*Vladislav Panferov*||
|[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|*Oladotun Aluko*||
|[ ] |“Why Should I Trust You?” Explaining the Predictions of Any Classifier, https://arxiv.org/pdf/1602.04938.pdf, https://github.com/marcotcr/lime|*Rohan Kumar Rathore*|?|
|[ ]Manifold MixUp| Manifold Mixup: Better Representations by Interpolating Hidden States. URL: https://arxiv.org/pdf/1806.05236v4 || |
|[ ]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|*Alix Bernard*||
|Artistic Style|Gatys L. A., Ecker A. S., Bethge M. A neural algorithm of artistic style //arXiv preprint arXiv:1508.06576. – 2015.|*Elena Voskoboy*|
|\4=.*Fluid, Oil, Physics, Chemistry*|
|[ ]Data-driven predictive using field inversion|Parish, Eric J., and Karthik Duraisamy. "A paradigm for data-driven predictive modeling using field inversion and machine learning." Journal of Computational Physics 305 (2016): 758-774.|*Omid Razizadeh*|*31-Oct*|
|[ ]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|||
|\4=.*Faces*|
|[ ]SphereFace| SphereFace: Deep Hypersphere Embedding for Face Recognition URL: https://arxiv.org/pdf/1704.08063.pdf|*Mukul Vishwas*| |
|[x]Triplet Loss|https://arxiv.org/pdf/1503.03832.pdf|*Vassily Baranov*||
|[x]Style transfer SotA (state-of-the-art)| A Style-Based Generator Architecture for Generative Adversarial Networks. URL: https://arxiv.org/abs/1812.04948 |*Michael Rodin*|5-Dec|
|Performance of Word Embeddings|review and experience|||
|[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|*Mikhail Liz*||
|\4=.*Quantum*|
|Supervised learning with quantum enhanced feature spaces|Havlíček V. et al. Supervised learning with quantum-enhanced feature spaces //Nature. – 2019. – Т. 567. – №. 7747. – С. 209. URL: https://arxiv.org/pdf/1804.11326.pdf|*Raphael Blankson*|12-Dec|
|FermiNet|Ab-Initio Solution of the Many-Electron Schr\" odinger Equation with Deep Neural Networks, https://arxiv.org/pdf/1909.02487|*Kristanek Antoine*||
|[ ]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|||
|[ ]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|||
|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|*Andrey Yashkin*||
|\4=.*Economics*|
|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|*Abhishek Saxena*, *Watana Pongsapas*||
||Li X. et al. Empirical analysis: stock market prediction via extreme learning machine //Neural Computing and Applications. – 2016. – Т. 27. – №. 1. – С. 67-78.|*Kaivalya Anand Pandey*, *Rishabh Tiwari*||
|\4=.*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|||
|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|*Nikita Nikolaev*||
|\4=.*Natural Language Processing*|
|[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 |*Alexander Donets*||
|[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|*Alexey Korolev*||
|[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|*Alexander Rusnak*||
|ELMo|Peters M. E. et al. Deep contextualized word representations //arXiv preprint arXiv:1802.05365. – 2018. URL: http://www.aclweb.org/anthology/N18-1202|*Sergey Garmaev*||
|[ ]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|||
|[ ]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|||
|[ ]Vision and Feature Norm|Vision and Feature Norms: Improving automatic feature norm learning through cross-modal maps. URL: https://aclweb.org/anthology/N16-1071|*Dinesh Reddy*|7-Nov|
|[x]Reinforcement Learning|Human-level control through deep reinforcement learning|*Kirill Kalmutskiy*||
||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.|||
|\4=.*Papers with code*|
|Weight Agnostic Neural Networks|Weight Agnostic Neural Networks, Google, https://arxiv.org/abs/1906.04358|*Roman |CBNet|CBNet A Novel composite, https://arxiv.org/abs/1909.03625|*Roman Kozinets*|*14-Nov*|
|SeqSleepNet|Phan H. et al. SeqSleepNet: end-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging //IEEE Transactions on Neural Systems and Rehabilitation Engineering. – 2019. – Т. 27. – №. 3. – С. 400-410.|*Daria Pirozhkova*||
|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*|
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 to-be-listed | |-|
|\3=.2st year students|
|1 Razizadeh Omid | ||
|2 Siyoto Owen | ||
|3 Munyaradzi Njera | ||
|4 Averyanov Evgeniy | ||
|5 Kozinets Roman | |*14-Nov*|
|6 Tagirova Elizaveta | |*19-Dec*|
|7 Tsvaki Jetina | ||
|8 Ravi Kumar | ||
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
* *noname*: refer, master
2nd year students
* *noname*: refer, master
h1. 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]].