Uncertainty
Total stochastic gradient algorithms and applications in reinforcement learning
Neural Information Processing Systems http://nips.cc/
Deep State Space Models for Unconditional Word Generation
Deep generative models for sequential data are an active field of research.
Maximum-Entropy Fine Grained Classification
Abhimanyu Dubey, Otkrist Gupta, Ramesh Raskar, Nikhil Naik
Fine-Grained Visual Classification (FGVC) is an important computer vision problem that involves small diversity within the different classes, and often requires expert annotators to collect data.
A Bayesian Nonparametric View on Count-Min Sketch
Diana Cai, Michael Mitzenmacher, Ryan P. Adams
Using simulated data and text data, we investigate the properties of these estimators. Lastly, we also study a modified problem in which the observation stream consists of collections of tokens (i.e., documents) arising from a random measure drawn from a stable beta process,
Distributed Weight Consolidation: A Brain Segmentation Case Study
Patrick McClure, Charles Y. Zheng, Jakub Kaczmarzyk, John Rogers-Lee, Satra Ghosh, Dylan Nielson, Peter A. Bandettini, Francisco Pereira
Neural Information Processing Systems http://nips.cc/
Differentially Private Bayesian Inference for Exponential Families
Garrett Bernstein, Daniel R. Sheldon
Neural Information Processing Systems http://nips.cc/
A General Method for Amortizing Variational Filtering
Joseph Marino, Milan Cvitkovic, Yisong Yue
The algorithm is derived from the variational objective in the filtering setting and consists of an optimization procedure at each time step.
Leveraging the Exact Likelihood of Deep Latent Variable Models
Pierre-Alexandre Mattei, Jes Frellsen
The purpose of this work is to study the general properties of this quantity and to show how they can be leveraged in practice.
Identification and Estimation of Causal Effects from Dependent Data
We then demonstrate how statistical inference may be performed on causal parameters identified by this algorithm.
Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net
Neural Information Processing Systems http://nips.cc/