Learning Graphical Models
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
Quantifying predictive uncertainty in NNs is a challenging and yet unsolved problem.
Learning Disentangled Representations with Semi-Supervised Deep Generative Models
Siddharth N, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah Goodman, Pushmeet Kohli, Frank Wood, Philip Torr
We evaluate our framework's ability
Multiscale Semi-Markov Dynamics for Intracortical Brain-Computer Interfaces
Daniel Milstein, Jason Pacheco, Leigh Hochberg, John D. Simeral, Beata Jarosiewicz, Erik Sudderth
We also introduce a multimodal likelihood model for recordings of neural populations which can be rapidly calibrated for clinical applications.
Probabilistic Models for Integration Error in the Assessment of Functional Cardiac Models
Chris Oates, Steven Niederer, Angela Lee, François-Xavier Briol, Mark Girolami
The computational model can be assessed through comparison of these predictions to test data generated from an experiment.
Estimating Accuracy from Unlabeled Data: A Probabilistic Logic Approach
Emmanouil Platanios, Hoifung Poon, Tom M. Mitchell, Eric J. Horvitz
Neural Information Processing Systems http://nips.cc/
Causal Effect Inference with Deep Latent-Variable Models
Christos Louizos, Uri Shalit, Joris M. Mooij, David Sontag, Richard Zemel, Max Welling
Examples include understanding the effect of medications on a patient's health, or of teaching methods on a student's chance of graduation.
Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity
As observed by them, this "assumption
Regret Minimization in MDPs with Options without Prior Knowledge
However, incorporating options does not always improve learning efficiency as shown by Jong et al.
Z-Forcing: Training Stochastic Recurrent Networks
Anirudh Goyal ALIAS PARTH GOYAL, Alessandro Sordoni, Marc-Alexandre Côté, Nan Rosemary Ke, Yoshua Bengio
Stochastic recurrent models have been successful in capturing the variability observed in natural sequential data such as speech.
Subset Selection and Summarization in Sequential Data
Ehsan Elhamifar, M. Clara De Paolis Kaluza
However, nearly all existing subset selection techniques ignore underlying dynamics of data and treat items independently, leading to incompatible sets of representatives.