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TopicNameAffiliationCurrent academic status (e.g post doc, full professor)First date I will be able to presentAm I planning to present original work or an existing paper ?(both are fine!!)Resources: idea for papersResources: blogs Resources: github / code implementations
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Machine learning applications
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GANs and their variants.
https://arxiv.org/abs/1905.01164
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NLP: BERT, XLnet and beyond.
https://arxiv.org/abs/1910.03771
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Modern image segmentation.
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Recent developments in 2D and 3D pose estimation
https://arxiv.org/abs/1909.12224
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Sound synthesis and generationhttps://arxiv.org/abs/1609.03499; https://arxiv.org/pdf/1802.04208.pdf?
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Video-to-video synthesis
https://arxiv.org/abs/1910.12713; https://arxiv.org/abs/1910.09139
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Architectures for symbolic music data (e.g., Music Transformer)https://www.ai.google/research/pubs/pub47717; https://arxiv.org/pdf/1612.01010.pdf
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Mixed models (explicit layers + learnable layers)
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Learning systems and techniques
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Continuous learning
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Probabilistic programming and deep learning
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Deep learning and control
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Deep learning and system design
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Neural Turing machines
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RL and its usage in deep learning
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Alpha zero - and recent developments in general AI for board games.
https://arxiv.org/pdf/1712.01815.pdf
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Designing neural network through neuroevolution (e.g Stanley et al. 2019)
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Transformers and their usage
https://arxiv.org/abs/1910.03771
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Deep CCA
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Recent developments in AI safety
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Boosting
https://arxiv.org/abs/1603.02754
https://towardsdatascience.com/catboost-vs-light-gbm-vs-xgboost-5f93620723db
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Machines and mind
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Dynamical systems for representation of neural data
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The practice of brain machine interfaces
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Deep learning models of high-level auditory/visual neural processing
https://doi.org/10.1073/pnas.1403112111; https://doi.org/10.1038/nn.4244; https://doi.org/10.1016/j.neuron.2018.03.044; https://doi.org/10.1038/s41593-019-0520-2
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Deep neural networks as cognitive modelshttps://arxiv.org/abs/1911.09288; https://arxiv.org/abs/1905.09397
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The theoretical grounding of pattern similarity and similar imaging techniques
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Sampling algorithms that combines humans in the loop (MCMCP)Nori JacobyMPIEAGroup leader2/2020Mainly papers by Griffiths et al. some new applications (work in progress)Sanborn and Griffiths 2008 https://papers.nips.cc/paper/3214-markov-chain-monte-carlo-with-people.pdf
https://pythonhosted.org/dallinger/demos/mcmcp/index.html
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Physical modeling - ADEPT
http://www.mit.edu/~k2smith/publication/adept/; https://arxiv.org/abs/1806.08047
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Recent developments in theory of machine learning
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Information bottleneck perspective on deep learning
https://arxiv.org/abs/1503.02406
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Deep Gaussian processes
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Optimization for deep learning
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Modern techniques for interpretabilityhttps://arxiv.org/abs/1910.13140; https://dl.acm.org/citation.cfm?id=3330886; https://arxiv.org/abs/1907.10882
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