Search Results for author:
Found 13 papers, 1 papers with code
Weakly-Supervised Semantic Segmentation of Circular-Scan, Synthetic-Aperture-Sonar Imagery
We propose a weakly-supervised framework for the semantic segmentation of circular-scan synthetic-aperture-sonar (CSAS) imagery.
Adapting the Exploration Rate for Value-of-Information-Based Reinforcement Learning
We then illustrate that these trends hold for deep, value-of-information-based agents that learn to play ten simple games and over forty more complicated games for the Nintendo GameBoy system.
Estimating Rényi's $α$-Cross-Entropies in a Matrix-Based Way
This yields matrix-based estimators of R\'enyi's $\alpha$-cross-entropies.
External-Memory Networks for Low-Shot Learning of Targets in Forward-Looking-Sonar Imagery
We propose a memory-based framework for real-time, data-efficient target analysis in forward-looking-sonar (FLS) imagery.
An Information-Theoretic Approach for Automatically Determining the Number of States when Aggregating Markov Chains
A fundamental problem when aggregating Markov chains is the specification of the number of state groups.
Annotating Motion Primitives for Simplifying Action Search in Reinforcement Learning
We have previously developed a means of constraining, and hence speeding up, the search process through the use of motion primitives; motion primitives are sequences of pre-specified actions taken across a state series.
Faster Convergence in Deep-Predictive-Coding Networks to Learn Deeper Representations
It yields unsupervised object recognition that surpass convolutional autoencoders and are on par with convolutional networks trained in a supervised manner.
Target Detection and Segmentation in Circular-Scan Synthetic-Aperture-Sonar Images using Semi-Supervised Convolutional Encoder-Decoders
We show that our framework outperforms supervised, deep-saliency networks designed for natural imagery.
An Exact Reformulation of Feature-Vector-based Radial-Basis-Function Networks for Graph-based Observations
An important property of our radial basis function networks is that they are guaranteed to yield the same responses as conventional radial-basis networks trained on a corresponding vector realization of the relationships encoded by the adjacency-matrix.
Guided Policy Exploration for Markov Decision Processes using an Uncertainty-Based Value-of-Information Criterion
The value of information yields a stochastic routine for choosing actions during learning that can explore the policy space in a coarse to fine manner.
Partitioning Relational Matrices of Similarities or Dissimilarities using the Value of Information
In this paper, we provide an approach to clustering relational matrices whose entries correspond to either similarities or dissimilarities between objects.
An Analysis of the Value of Information when Exploring Stochastic, Discrete Multi-Armed Bandits
High amounts of policy information are associated with exploration-dominant searches of the space and yield high rewards.
Analysis of Agent Expertise in Ms. Pac-Man using Value-of-Information-based Policies
This cost function is the value of information, which provides the optimal trade-off between the expected return of a policy and the policy's complexity; policy complexity is measured by number of bits and controlled by a single hyperparameter on the cost function.
