ML sessions 2018

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 Maarkus 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

-------------------------------------------------------------------------------- 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

-------------------------------------------------------------------------------- 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

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