Probabilistic Deep Learning #1 - 16. Nov. 2017

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://arxiv.org/abs/1506.02142

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State-of-the-art ML #5 - 02. Nov. 2017

Learning from Simulated and Unsupervised Images through Adversarial Training

https://arxiv.org/abs/1612.07828

State-of-the-art ML #4 - 19. Oct. 2017

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

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

State-of-the-art ML #3 - 05. Oct. 2017

Balancing information exposure in social networks

https://arxiv.org/pdf/1709.01491.pdf

State-of-the-art ML #2 - 21. Sep. 2017

The numerics of GAN

https://arxiv.org/pdf/1705.10461.pdf

State-of-the-art ML #1 - 07. Sep. 2017

Deep Gaussian Processes

http://proceedings.mlr.press/v31/damianou13a.pdf​

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Inference in Bayesian Networks #6 - 29. June 2017

• Fast nonparametric clustering of structured time-series" by James Hensman, Magnus Rattray and Neil D. Lawrence - Olga
• Deep Gaussian Process Andreas, C. Damianou and Neil D. Lawrence - Judith
• Pitkow, X., & Angelaki, D. E. (2017). How the Brain Might Work: Statistics Flowing in Redundant Population Codes. - Ylva

Inference in Bayesian Networks #5 - 15. June 2017

 An Introduction to MCMC for Machine Learning - C. Andrieu - ‎2003

http://mathfaculty.fullerton.edu/sbehseta/Math470-10.1.1.13.7133.pdf

Inference in Bayesian Networks #4 - 01. June 2017

Approximate Bayesian computational methods

http://link.springer.com/article/10.1007%2Fs11222-011-9288-2?LI=true

Inference in Bayesian Networks #3 - 18. May 2017

Hierarchical Beta Processes and the Indian Buffet Process (Monte Carlo inference scheme)

http://people.ee.duke.edu/~lcarin/thibaux-jordan-aistats07.pdf

Inference in Bayesian Networks #2 - 04. May 2017

Expectation Propagation for approximate Bayesian inference

https://arxiv.org/abs/1301.2294

Inference in Bayesian Networks #1 - 20. Apr. 2017

Understanding Belief Propagation and its Generalizations

http://www.merl.com/publications/docs/TR2001-22.pdf

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Transfer Learning #6 - 05. Apr. 2017

Application session

Marcus and Judith
"Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping"

Vladimir and Ondrej
"Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuning"

Sahar
"Domain Randomization for transferring deep neural networks from simulations to real world"

Silvia and Erik
"Effective Transfer Learning of Affordances for Household Robots"

Taras and Joonatan
"Sim-to-Real Robot Learning from Pixels with Progressive Nets"

Olga
"Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks"

Ylva and Ramon
"How Transferable Are Features in Deep Neural Networks"

Transfer Learning #5 - 23. Mar. 2017

A theory of learning from different domains

http://www.alexkulesza.com/pubs/adapt_mlj10.pdf

Transfer Learning #4 - 9. Mar. 2017

Understanding deep learning requires rethinking generalization

https://arxiv.org/pdf/1611.03530

Transfer Learning #3 - 23. Feb. 2017

 DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition

http://jmlr.org/proceedings/papers/v32/donahue14.pdf

Transfer Learning #2 - 9. Feb. 2017

Learning to learn by gradient descent by gradient descent

https://arxiv.org/abs/1606.04474

Transfer Learning #1 - 25. Jan. 2017

A Survey on Transfer Learning

https://www.cse.ust.hk/~qyang/Docs/2009/tkde_transfer_learning.pdf

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Approximate inference #6 - 12. Jan. 2017

Black-Box Variational Inference

http://www.cs.columbia.edu/~blei/papers/RanganathGerrishBlei2014.pdf

Approximate inference #5 - 15. Dec. 2016 - Christmas edition

Variational Tempering

http://jmlr.org/proceedings/papers/v51/mandt16.pdf

Approximate inference #4 - 24. Nov. 2016

Variational Message Passing 

http://www.jmlr.org/papers/volume6/winn05a/winn05a.pdf

Approximate inference #3 - 10. Nov. 2016

Stochastic Variational Inference

http://jmlr.org/papers/volume14/hoffman13a/hoffman13a.pdf

Approximate inference #2 - 27. Oct. 2016

An Introduction to Variational Methodsfor Graphical Models

https://people.eecs.berkeley.edu/~jordan/papers/variational-intro.pdf

Approximate inference #1 - 13. Oct. 2016

Graphical Models

https://www.cs.cmu.edu/~aarti/Class/10701/readings/graphical_model_Jordan.pdf

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Deep Learning #10 - 29. Sep. 2016

Deep Learning #9 - 15. Sep. 2016

Learning to Communicate withDeep Multi-Agent Reinforcement Learning

https://arxiv.org/pdf/1605.06676v2.pdf

Deep Learning #8 - 23 June 2016 / 01. Sep. 2016

End-to-End Training of Deep Visuomotor Policies

https://arxiv.org/pdf/1504.00702v5.pdf

Deep Learning #7 - 9 June 2016

Benchmarking Deep Reinforcement Learning for Continuous Control

http://arxiv.org/pdf/1604.06778v2.pdf

Continuous control with deep reinforcement learning

http://arxiv.org/pdf/1509.02971v5.pdf

Deep Learning #6 - 26 May 2016

Playing atari with deep reinforcement learning 

http://arxiv.org/abs/1312.5602

Mastering the game of Go with deep neural networks and tree search

http://www.nature.com/nature/journal/v529/n7587/full/nature16961.html

Deep Learning #5 - 12 May 2016

Variational Auto-encoded Deep Gaussian Processes
  http://arxiv.org/abs/1511.06455

Deep Learning #4 - 21 April 2016

Deep Learning #3 - 7 April 2016

ADVERSARIAL AUTOENCODERS- Goodfellow et.al (2016)

Deep Learning #2 - 24 March 2016

Represenation Learning: A Review and New Perspectives - Vincent et. al. (2014)

Deep Learning #1 - 10 March 2016

Deep Learning - Hinton et. al. (Nature, 2015)

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Applications of Probabilistic Numerics and Bayesian Optimization (25 February 2016)

Jim Holmström, Erik Ward:
"Designing Engaging Games Using Bayesian Optimization"

Silvia Cruciani, Ali Ghadirzadeh:
"A Bayesian Exploration-Exploitation Approach for Optimal Online Sensing and Planning with a Visually Guided Mobile Robot"

Cheng Zhang, Judith Butepage:
"Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction"

Anastasiia Varav, João ... de Carvalho
"Metrics for Probabilistic Geometries"

Yanxia Zhang, Puren Guler
"Robots that can adapt like animals"