Browsing by Subject "Artificial neural networks"
Now showing items 1-8 of 8
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An actor-critic reinforcement learning approach for bilateral negotiation
Designing an effective and intelligent bidding strategy is one of the most compelling research challenges in automated negotiation, where software agents negotiate with each other to find a mutual agreement when there is ... -
Calibrating artificial neural networks by global optimization
(Elsevier, 2012-01)Artificial neural networks (ANNs) are used extensively to model unknown or unspecified functional relationships between the input and output of a “black box” system. In order to apply the generic ANN concept to actual ... -
Calibrating artificial neural networks by global optimization
(2010-07)An artificial neural network (ANN) is a computational model − implemented as a computer program − that is aimed at emulating the key features and operations of biological neural networks. ANNs are extensively used to model ... -
Clothing image retrieval with triplet capsule networks
(2019-08-19)Clothing image retrieval has become more important after some major developments in Computer Science and the emergence of e-commerce. Recent studies generally attack this problem by using Convolutional Neural Networks ... -
Explorations on inverse reinforcement learning for the analysis of motor control and cognitive decision making mechanisms of the brain
Reinforcement Learning is a framework for generating optimal policies given a task and a reward/punishment structure. Likewise, Inverse Reinforcement Learning, as the name suggests, is used for recovering the reasoning ... -
Speaker adaptation with deep learning for text-to-speech synthesis systems
End-to-end (e2e) speech synthesis systems have become popular with the recent introduction of letter-to-spectrogram conversion systems, such as Tacotron, that use encoder-decoder-based neural architectures. Even though ... -
Using artificial neural networks to provide guidance in extending PL/SQL programs
(Springer, 2022-12)Extending legacy systems with new objects for contemporary functionality or technology can lead to architecture erosion. Misplacement of these objects gradually hampers the modular structure, of which documentation is ... -
Weight update skipping: Reducing training time for artificial neural networks
(IEEE, 2021-12)Artificial Neural Networks (ANNs) are known as state-of-the-art techniques in Machine Learning (ML) and have achieved outstanding results in data-intensive applications, such as recognition, classification, and segmentation. ...
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