Why should i trust you?: Explaining the predictions of any classifier.
Sequential Neural Models with Stochastic Layers
http://papers.nips.cc/paper/6039-sequential-neural-models-with-stochastic-layers.pdf
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
https://arxiv.org/pdf/1803.01271.pdf
Neural Turing Machines
https://arxiv.org/pdf/1410.5401.pdf
Sequence modeling: Recurrent and recursive nets (page: 388-415)
http://www.deeplearningbook.org/contents/rnn.html
Sequence modeling: Recurrent and recursive nets (page: 367-388)
http://www.deeplearningbook.org/contents/rnn.html
The limits and potentials of deep learning for robotics
http://journals.sagepub.com/doi/full/10.1177/0278364918770733
The Supervised Learning No-Free-Lunch Theorems
web.mit.edu/6.435/www/Dempster77.pdf
Maximum Likelihood from Incomplete Data via the EM Algorithm
http://web.mit.edu/6.435/www/Dempster77.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"
Anastasiia Varav, João ... de Carvalho
"Metrics for Probabilistic Geometries"
Yanxia Zhang, Puren Guler
"Robots that can adapt like animals"
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