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Planned Readings

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

Deep Learning #10 - 29. Sep. 2016

Deep learning application setting

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

Long Short-Term Memory

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)

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"