AIMC Topic: Neural Networks, Computer

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State-trace analysis: dissociable processes in a connectionist network?

Cognitive science
Some argue the common practice of inferring multiple processes or systems from a dissociation is flawed (Dunn, 2003). One proposed solution is state-trace analysis (Bamber, 1979), which involves plotting, across two or more conditions of interest, pe...

Do lemmas speak German? A verb position effect in German structural priming.

Cognitive science
Lexicalized theories of syntax often assume that verb-structure regularities are mediated by lemmas, which abstract over variation in verb tense and aspect. German syntax seems to challenge this assumption, because verb position depends on tense and ...

Approximate N-Player Nonzero-Sum Game Solution for an Uncertain Continuous Nonlinear System.

IEEE transactions on neural networks and learning systems
An approximate online equilibrium solution is developed for an N -player nonzero-sum game subject to continuous-time nonlinear unknown dynamics and an infinite horizon quadratic cost. A novel actor-critic-identifier structure is used, wherein a robus...

Artificial neural network modelling of biological oxygen demand in rivers at the national level with input selection based on Monte Carlo simulations.

Environmental science and pollution research international
Biological oxygen demand (BOD) is the most significant water quality parameter and indicates water pollution with respect to the present biodegradable organic matter content. European countries are therefore obliged to report annual BOD values to Eur...

Opportunistic Behavior in Motivated Learning Agents.

IEEE transactions on neural networks and learning systems
This paper focuses on the novel motivated learning (ML) scheme and opportunistic behavior of an intelligent agent. It extends previously developed ML to opportunistic behavior in a multitask situation. Our paper describes the virtual world implementa...

Adaptive Batch Mode Active Learning.

IEEE transactions on neural networks and learning systems
Active learning techniques have gained popularity to reduce human effort in labeling data instances for inducing a classifier. When faced with large amounts of unlabeled data, such algorithms automatically identify the exemplar and representative ins...

Measuring the usefulness of hidden units in Boltzmann machines with mutual information.

Neural networks : the official journal of the International Neural Network Society
Restricted Boltzmann machines (RBMs) and deep Boltzmann machines (DBMs) are important models in deep learning, but it is often difficult to measure their performance in general, or measure the importance of individual hidden units in specific. We pro...

Expected energy-based restricted Boltzmann machine for classification.

Neural networks : the official journal of the International Neural Network Society
In classification tasks, restricted Boltzmann machines (RBMs) have predominantly been used in the first stage, either as feature extractors or to provide initialization of neural networks. In this study, we propose a discriminative learning approach ...

Two-layer contractive encodings for learning stable nonlinear features.

Neural networks : the official journal of the International Neural Network Society
Unsupervised learning of feature hierarchies is often a good strategy to initialize deep architectures for supervised learning. Most existing deep learning methods build these feature hierarchies layer by layer in a greedy fashion using either auto-e...

A one-layer recurrent neural network for constrained nonconvex optimization.

Neural networks : the official journal of the International Neural Network Society
In this paper, a one-layer recurrent neural network is proposed for solving nonconvex optimization problems subject to general inequality constraints, designed based on an exact penalty function method. It is proved herein that any neuron state of th...