AIMC Journal:
IEEE transactions on neural networks and learning systems

Showing 561 to 570 of 814 articles

Completely Automated CNN Architecture Design Based on Blocks.

IEEE transactions on neural networks and learning systems Jun 20, 2019
The performance of convolutional neural networks (CNNs) highly relies on their architectures. In order to design a CNN with promising performance, extensive expertise in both CNNs and the investigated problem domain is required, which is not necessar...
Random Allocation Pattern Recognition, Automated Neural Networks, Computer Humans Databases, Factual Algorithms
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A Probabilistic Synapse With Strained MTJs for Spiking Neural Networks.

IEEE transactions on neural networks and learning systems Jun 18, 2019
Spiking neural networks (SNNs) are of interest for applications for which conventional computing suffers from the nearly insurmountable memory-processor bottleneck. This paper presents a stochastic SNN architecture that is based on specialized logic-...
Probability Synapses Action Potentials Neural Networks, Computer Neuronal Plasticity Machine Learning Humans
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Where Computation and Dynamics Meet: Heteroclinic Network-Based Controllers in Evolutionary Robotics.

IEEE transactions on neural networks and learning systems Jun 18, 2019
In the fields of artificial neural networks and robotics, complicated, often high-dimensional systems can be designed using evolutionary/other algorithms to successfully solve very complex tasks. However, dynamical analysis of the underlying controll...
Robotics Algorithms Neural Networks, Computer
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Self-Paced Balance Learning for Clinical Skin Disease Recognition.

IEEE transactions on neural networks and learning systems Jun 12, 2019
Class imbalance is a challenging problem in many classification tasks. It induces biased classification results for minority classes that contain less training samples than others. Most existing approaches aim to remedy the imbalanced number of insta...
Humans Skin Diseases Algorithms Databases, Factual Pattern Recognition, Automated Machine Learning
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Deep Neural Architectures for Highly Imbalanced Data in Bioinformatics.

IEEE transactions on neural networks and learning systems Jun 3, 2019
In the postgenome era, many problems in bioinformatics have arisen due to the generation of large amounts of imbalanced data. In particular, the computational classification of precursor microRNA (pre-miRNA) involves a high imbalance in the classes. ...
Databases, Factual Humans Animals Neural Networks, Computer Plants Deep Learning Elasticity Computational Biology
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Heterogeneous Multilayer Generalized Operational Perceptron.

IEEE transactions on neural networks and learning systems May 31, 2019
The traditional multilayer perceptron (MLP) using a McCulloch-Pitts neuron model is inherently limited to a set of neuronal activities, i.e., linear weighted sum followed by nonlinear thresholding step. Previously, generalized operational perceptron ...
Neural Networks, Computer Databases, Factual Algorithms
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Compact and Computationally Efficient Representation of Deep Neural Networks.

IEEE transactions on neural networks and learning systems May 29, 2019
At the core of any inference procedure, deep neural networks are dot product operations, which are the component that requires the highest computational resources. For instance, deep neural networks, such as VGG-16, require up to 15-G operations in o...
Deep Learning Neural Networks, Computer Entropy
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Cognitive Action Laws: The Case of Visual Features.

IEEE transactions on neural networks and learning systems May 8, 2019
This paper proposes a theory for understanding perceptual learning processes within the general framework of laws of nature. Artificial neural networks are regarded as systems whose connections are Lagrangian variables, namely, functions depending on...
Pattern Recognition, Automated Photic Stimulation Neural Networks, Computer Machine Learning Cognition
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Recurrent Neural Networks With External Addressable Long-Term and Working Memory for Learning Long-Term Dependences.

IEEE transactions on neural networks and learning systems May 3, 2019
Learning long-term dependences (LTDs) with recurrent neural networks (RNNs) is challenging due to their limited internal memories. In this paper, we propose a new external memory architecture for RNNs called an external addressable long-term and work...
Neural Networks, Computer Memory, Short-Term Databases, Factual Memory, Long-Term
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Selection and Optimization of Temporal Spike Encoding Methods for Spiking Neural Networks.

IEEE transactions on neural networks and learning systems Apr 12, 2019
Spiking neural networks (SNNs) receive trains of spiking events as inputs. In order to design efficient SNN systems, real-valued signals must be optimally encoded into spike trains so that the task-relevant information is retained. This paper provide...
Signal-To-Noise Ratio Models, Neurological Signal Processing, Computer-Assisted Algorithms Neural Networks, Computer Reproducibility of Results Action Potentials
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