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ML Sessions 2018


Interpretable ML #4 - 12. Dec. 2018

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 ML #3 - 29. Nov. 2018

Interpretable Explanations of Black Boxes by Meaningful Perturbation

https://www.robots.ox.ac.uk/~vedaldi//assets/pubs/fong17interpretable.pdf

Interpretable ML #2 - 15. Nov. 2018

Why should i trust you?: Explaining the predictions of any classifier.

https://arxiv.org/pdf/1602.04938v1.pdf

Interpretable ML #1 - 1. Nov. 2018

Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning

https://arxiv.org/pdf/1806.00069.pdf

RNNs #5 - Applications - 18. Oct. 2018


RNNs #5 - 04. Oct. 2018

Sequential Neural Models with Stochastic Layers 

http://papers.nips.cc/paper/6039-sequential-neural-models-with-stochastic-layers.pdf

RNNs #4 - 20. Sep. 2018

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

https://arxiv.org/pdf/1803.01271.pdf

RNNs #3 - 06. Sep. 2018

Neural Turing Machines

https://arxiv.org/pdf/1410.5401.pdf

RNNs #2 - 14. June. 2018

Sequence modeling: Recurrent and recursive nets (page: 388-415)

http://www.deeplearningbook.org/contents/rnn.html

RNNs #1 - 31. May. 2018

Sequence modeling: Recurrent and recursive nets  (page: 367-388)  

http://www.deeplearningbook.org/contents/rnn.html

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Something else - 17. May. 2018

 The limits and potentials of deep learning for robotics

http://journals.sagepub.com/doi/full/10.1177/0278364918770733

Optimization for ML #5 - 03. May. 2018

The Supervised Learning No-Free-Lunch Theorems

web.mit.edu/6.435/www/Dempster77.pdf

Optimization for ML #5 - 19. Apr. 2018

Maximum Likelihood from Incomplete Data via the EM Algorithm

http://web.mit.edu/6.435/www/Dempster77.pdf

Optimization for ML #3 - 05. Apr. 2018

Sharp Minima Can Generalize For Deep Nets

https://arxiv.org/pdf/1703.04933.pdf

Optimization for ML #2 - 08. Mar. 2018

Support Vector Machines

http://cs229.stanford.edu/notes/cs229-notes3.pdf

Optimization for ML #1 - 22. Feb. 2018

Large-Scale Machine Learning with Stochastic Gradient Descent

https://link.springer.com/content/pdf/10.1007%2F978-3-7908-2604-3_16.pdf

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Probabilistic Deep Learning #6 - 08. Feb. 2018

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

Probabilistic Deep Learning #5 - 25. Jan. 2018

Weight Uncertainty in Neural Networks

http://proceedings.mlr.press/v37/blundell15.pdf

Probabilistic Deep Learning #4 - 11. Jan. 2018

Priors for infinite networks

https://link.springer.com/content/pdf/10.1007%2F978-1-4612-0745-0_2.pdf