Search Results for author:
Found 11 papers, 1 papers with code
A Probabilistic Framework for Nonlinearities in Stochastic Neural Networks
We present a probabilistic framework for nonlinearities, based on doubly truncated Gaussian distributions.
Linear Feature Encoding for Reinforcement Learning
We then develop a supervised linear feature encoding method that is motivated by insights from linear value function approximation theory, as well as empirical successes from deep RL.
Unsupervised Learning with Truncated Gaussian Graphical Models
Gaussian graphical models (GGMs) are widely used for statistical modeling, because of ease of inference and the ubiquitous use of the normal distribution in practical approximations.
Nonlinear Statistical Learning with Truncated Gaussian Graphical Models
We introduce the truncated Gaussian graphical model (TGGM) as a novel framework for designing statistical models for nonlinear learning.
Variational Gaussian Copula Inference
We utilize copulas to constitute a unified framework for constructing and optimizing variational proposals in hierarchical Bayesian models.
Stick-Breaking Policy Learning in Dec-POMDPs
Expectation maximization (EM) has recently been shown to be an efficient algorithm for learning finite-state controllers (FSCs) in large decentralized POMDPs (Dec-POMDPs).
Compressive Sensing of Signals from a GMM with Sparse Precision Matrices
This paper is concerned with compressive sensing of signals drawn from a Gaussian mixture model (GMM) with sparse precision matrices.
Low-Cost Compressive Sensing for Color Video and Depth
A simple and inexpensive (low-power and low-bandwidth) modification is made to a conventional off-the-shelf color video camera, from which we recover {multiple} color frames for each of the original measured frames, and each of the recovered frames can be focused at a different depth.
Integrated Non-Factorized Variational Inference
We present a non-factorized variational method for full posterior inference in Bayesian hierarchical models, with the goal of capturing the posterior variable dependencies via efficient and possibly parallel computation.
Adaptive Temporal Compressive Sensing for Video
This paper introduces the concept of adaptive temporal compressive sensing (CS) for video.
Learning to Explore and Exploit in POMDPs
In this paper we propose a dual-policy method for jointly learning the agent behavior and the balance between exploration exploitation, in partially observable environments.
