Planned Readings

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

---------------------------------------------------------------------------------------------------------- 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"

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

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