Directed Networks
Multi-view Matrix Factorization for Linear Dynamical System Estimation
Mahdi Karami, Martha White, Dale Schuurmans, Csaba Szepesvari
Maximum likelihood for LDS estimation, on the other hand, has several advantages.
Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons
Nikhil Parthasarathy, Eleanor Batty, William Falcon, Thomas Rutten, Mohit Rajpal, E.J. Chichilnisky, Liam Paninski
However, existing linear techniques for neural decoding may not fully reveal or exploit the fidelity of the neural signal.
Scalable Model Selection for Belief Networks
Zhao Song, Yusuke Muraoka, Ryohei Fujimaki, Lawrence Carin
The factorized asymptotic Bayesian ( FAB) approach has recently been shown as a scalable model-selection framework for latent variable models.
Gradients of Generative Models for Improved Discriminative Analysis of Tandem Mass Spectra
John T. Halloran, David M. Rocke
Neural Information Processing Systems http://nips.cc/
Predicting User Activity Level In Point Processes With Mass Transport Equation
Yichen Wang, Xiaojing Ye, Hongyuan Zha, Le Song
However, existing works are mostly problem specific, use heuristics, or simplify the stochastic nature of point processes.
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
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.
Learning Identifiable Gaussian Bayesian Networks in Polynomial Time and Sample Complexity
As observed by them, this "assumption
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.