Faster Convergence in Deep-Predictive-Coding Networks to Learn Deeper Representations.

Deep-predictive-coding networks (DPCNs) are hierarchical, generative models. They rely on feed-forward and feedback connections to modulate latent feature representations of stimuli in a dynamic and context-sensitive manner. A crucial element of DPCNs is a forward-backward inference procedure to uncover sparse, invariant features.

An Exact Reformulation of Feature-Vector-Based Radial-Basis-Function Networks for Graph-Based Observations.

Radial basis function (RBF) networks are traditionally defined for sets of vector-based observations. In this brief, we reformulate such networks so that they can be applied to adjacency-matrix representations of weighted, directed graphs that represent the relationships between object pairs. We restate the sum-of-squares objective function so that it is purely dependent on entries from the adjacency matrix.

Reduction of Markov Chains Using a Value-of-Information-Based Approach.

In this paper, we propose an approach to obtain reduced-order models of Markov chains. Our approach is composed of two information-theoretic processes. The first is a means of comparing pairs of stationary chains on different state spaces, which is done via the negative, modified Kullback-Leibler divergence defined on a model joint space.

Guided Policy Exploration for Markov Decision Processes Using an Uncertainty-Based Value-of-Information Criterion.

Reinforcement learning in environments with many action-state pairs is challenging. The issue is the number of episodes needed to thoroughly search the policy space. Most conventional heuristics address this search problem in a stochastic manner.

An Analysis of the Value of Information When Exploring Stochastic, Discrete Multi-Armed Bandits.

In this paper, we propose an information-theoretic exploration strategy for stochastic, discrete multi-armed bandits that achieves optimal regret. Our strategy is based on the value of information criterion. This criterion measures the trade-off between policy information and obtainable rewards.

Emergent trend detection in diurnal activity.

When monitoring elders' daily routines, it is desirable to track significant deviations from a baseline pattern, as consecutive, aberrant days may foreshadow a need for medical attention. However, many traditional, unsupervised methods for pattern classification are ill-suited for this task, as they are incapable for adapting to additive datasets. To surmount this shortcoming, we establish a framework for recognizing temporal trends in feature data extracted from passive sensors.