Learning Graphical Models
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.
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms
Marco Cusumano-Towner, Vikash K. Mansinghka
Approximate probabilistic inference algorithms are central to many fields. Examples include sequential Monte Carlo inference in robotics, variational inference in machine learning, and Markov chain Monte Carlo inference in statistics.
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
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/