IEEE transactions on neural networks and learning systems
Oct 27, 2021
In this article, we discuss H control for unknown nonlinear systems in discrete time. A discrete-time recurrent neural network is used to model the nonlinear system, and then, the H tracking control is applied based on the neural model. Since this ne...
Neural networks : the official journal of the International Neural Network Society
Sep 17, 2021
Spiking neural networks (SNNs) aim to replicate energy efficiency, learning speed and temporal processing of biological brains. However, accuracy and learning speed of such networks is still behind reinforcement learning (RL) models based on traditio...
External disturbance poses the primary threat to robot balance in dynamic environments. This paper provides a learning-based control architecture for quadrupedal self-balancing, which is adaptable to multiple unpredictable scenes of external continuo...
We argue that cognitive models can provide a common ground between human users and deep reinforcement learning (Deep RL) algorithms for purposes of explainable artificial intelligence (AI). Casting both the human and learner as cognitive models provi...
Learning to act in an environment to maximise rewards is among the brain's key functions. This process has often been conceptualised within the framework of reinforcement learning, which has also gained prominence in machine learning and artificial i...
IEEE transactions on neural networks and learning systems
Jul 6, 2021
We consider a human-in-the-loop scenario in the context of low-shot learning. Our approach was inspired by the fact that the viability of samples in novel categories cannot be sufficiently reflected by those limited observations. Some heterogeneous s...
Computational intelligence and neuroscience
May 26, 2021
Relation classification is an important semantic processing task in the field of natural language processing (NLP). Data sources generally adopt remote monitoring strategies to automatically generate large-scale training data, which inevitably causes...
. Both artificial and biological controllers experience errors during learning that are probabilistically distributed. We develop a framework for modeling distributions of errors and relating deviations in these distributions to neural activity.. The...
A common pitfall of current reinforcement learning agents implemented in computational models is in their inadaptability postoptimization. Najarro and Risi [Najarro E, Risi S. . 2020: 20719-20731, 2020] demonstrate how such adaptability may be salvag...
Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely adopted to investigate functional abnormalities in brain diseases. Rs-fMRI data is unsupervised in nature because the psychological and neurological labels are coarse-grain...
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