The Supervised Learning No-Free-Lunch Theorems
http://www.no-free-lunch.org/Wolp01a.pdf
https://arxiv.org/pdf/1703.04933.pdf
https://link.springer.com/content/pdf/10.1007%2F978-3-7908-2604-3_16.pdf
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
Weight Uncertainty in Neural Networks
http://proceedings.mlr.press/v37/blundell15.pdf
Priors for infinite networks
https://link.springer.com/content/pdf/10.1007%2F978-1-4612-0745-0_2.pdf
Deep Kernel Learning
http://proceedings.mlr.press/v51/wilson16.pdf
What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?
https://arxiv.org/pdf/1703.04977.pdf
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://arxiv.org/abs/1506.02142
Learning from Simulated and Unsupervised Images through Adversarial Training
https://arxiv.org/abs/1612.07828
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://arxiv.org/abs/1602.04938.pdf
Balancing information exposure in social networks
https://arxiv.org/pdf/1709.01491.pdf
The numerics of GAN
https://arxiv.org/pdf/1705.10461.pdf
Deep Gaussian Processes
http://proceedings.mlr.press/v31/damianou13a.pdf
An Introduction to MCMC for Machine Learning - C. Andrieu - 2003
http://mathfaculty.fullerton.edu/sbehseta/Math470-10.1.1.13.7133.pdf
Approximate Bayesian computational methods
http://link.springer.com/article/10.1007%2Fs11222-011-9288-2?LI=true
Hierarchical Beta Processes and the Indian Buffet Process (Monte Carlo inference scheme)
http://people.ee.duke.edu/~lcarin/thibaux-jordan-aistats07.pdf
https://arxiv.org/abs/1301.2294
Understanding Belief Propagation and its Generalizations
http://www.merl.com/publications/docs/TR2001-22.pdf
Sahar
Olga
A theory of learning from different domains
http://www.alexkulesza.com/pubs/adapt_mlj10.pdf
Understanding deep learning requires rethinking generalization
https://arxiv.org/pdf/1611.03530
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
http://jmlr.org/proceedings/papers/v32/donahue14.pdf
Learning to learn by gradient descent by gradient descent
https://arxiv.org/abs/1606.04474
A Survey on Transfer Learning
https://www.cse.ust.hk/~qyang/Docs/2009/tkde_transfer_learning.pdf
Black-Box Variational Inference
http://www.cs.columbia.edu/~blei/papers/RanganathGerrishBlei2014.pdf
Variational Message Passing
http://www.jmlr.org/papers/volume6/winn05a/winn05a.pdf
http://jmlr.org/papers/volume14/hoffman13a/hoffman13a.pdf
https://people.eecs.berkeley.edu/~jordan/papers/variational-intro.pdf
https://www.cs.cmu.edu/~aarti/Class/10701/readings/graphical_model_Jordan.pdf
Deep learning application session
https://arxiv.org/pdf/1605.06676v2.pdf
End-to-End Training of Deep Visuomotor Policies
https://arxiv.org/pdf/1504.00702v5.pdf
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
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
Long Short-Term Memory
ADVERSARIAL AUTOENCODERS- Goodfellow et.al (2016)
Represenation Learning: A Review and New Perspectives - Vincent et. al. (2014)
Deep Learning - Hinton et. al. (Nature, 2015)
Applications of Probabilistic Numerics and Bayesian Optimization ()
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"
![](https://webmail.kth.se/owa/service.svc/s/GetPersonaPhoto?email=varava%40kth.se&UA=0&size=HR96x96) Anastasiia Varav, João ... de Carvalho "Metrics for Probabilistic Geometries"
Yanxia Zhang, Puren Guler "Robots that can adapt like animals"
|