A Regularized Framework for Sparse and Structured Neural Attention
https://papers.nips.cc/paper/6926-a-regularized-framework-for-sparse-and-structured-neural-attention.pdf
Interpretable Explanations of Black Boxes by Meaningful Perturbation
https://www.robots.ox.ac.uk/~vedaldi//assets/pubs/fong17interpretable.pdf
Why should i trust you?: Explaining the predictions of any classifier.
Sequential Neural Models with Stochastic Layers
http://papers.nips.cc/paper/6039-sequential-neural-models-with-stochastic-layers.pdf
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://arxiv.org/pdf/1803.01271.pdf
Neural Turing Machines
https://arxiv.org/pdf/1410.5401.pdf
Sequence modeling: Recurrent and recursive nets (page: 388-415)
http://www.deeplearningbook.org/contents/rnn.html
Sequence modeling: Recurrent and recursive nets (page: 367-388)
http://www.deeplearningbook.org/contents/rnn.html
The limits and potentials of deep learning for robotics
http://journals.sagepub.com/doi/full/10.1177/0278364918770733
The Supervised Learning No-Free-Lunch Theorems
web.mit.edu/6.435/www/Dempster77.pdf
Maximum Likelihood from Incomplete Data via the EM Algorithm
http://web.mit.edu/6.435/www/Dempster77.pdf
https://arxiv.org/pdf/1703.04933.pdf
https://link.springer.com/content/pdf/10.1007%2F978-3-7908-2604-3_16.pdf
Application Session
- Bayesian Recurrent Neural Networks
- Learning & policy search in stochastic dynamical systems with BNNs
- Deep Probabilistic Programming
- Neural Discrete Representation Learning
- Deep Bayesian Active Learning with Image Data
Weight Uncertainty in Neural Networks
http://proceedings.mlr.press/v37/blundell15.pdf
Priors for infinite networks
https://link.springer.com/content/pdf/10.1007%2F978-1-4612-0745-0_2.pdf
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