Gruppen DD3012 Machine Learning Reading Course 6.0 credits

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

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention	Sofia
Explainable Neural Computation via Stack Neural Module Networks	Judith
Pixel Recurrent Neural Networks	Federico	Matteo
Deep Variational Reinforcement Learning for POMDPs (ICML2018)	Rika
Wisture: RNN-based Learning of Wireless Signals for Gesture Recognition in Unmodified Smartphones	Sahar	Dong
Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN)	Louise
A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues	Samuel	Markus
Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition	Sarah	Sanne
Wavenet: A Generative Model for Raw Audio	Isac
Recurrent Neural Network for Multivariate Time Series with Missing Values	Ruibo

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