Which artificial neural network allows loops?
Answers from top 10 papers
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03 Jun 1998-arXiv: Adaptation and Self-Organizing Systems
|This paper shows that a new type of artificial neural network (ANN) -- the Simultaneous Recurrent Network (SRN) -- can, if properly trained, solve a difficult function approximation problem which conventional ANNs -- either feedforward or Hebbian -- cannot.|
03 May 1993
|These building blocks can be applied to artificial neural network (ANN) design in particular and to analog signal processing in general.<>|
|An artificial neural network, a biologically inspired computing method which has an ability to learn, self-adjust, and be trained, provides a powerful tool in solving complex problems.|
01 May 2007-Water Resources Management
|In such event, artificial neural network (ANN) model can be a potential alternative to the conventional models.|
01 Dec 2017
|Artificial neural network (ANN) resembles brain biological neural network and can be used to simulate chaotic system.|
01 Jan 2005
|Artificial neural network, a biologically inspired computing method which has an ability to learn, self-adjust, and be trained, provides a powerful tool in solving pattern recognition problems.|
01 May 2014-Neural Computing and Applications
|Artificial neural network (ANN) is another promising alternative with the unique capability of nonlinear self-adaptive modeling.|
|Among these, the artificial neural network (NN) system appears to be a powerful tool to tackle situations in w...|
04 Jan 2017-Aircraft Engineering and Aerospace Technology
|This kind of network combines or better fuses the advantages of backpropagation artificial neural algorithm and Hu moment.|
08 May 1989
|One popular artificial neural network model, the back-propagation algorithm, promises to be a powerful and flexible learning model.|
What is human-in-the-loop ai?5 answersHuman-in-the-loop AI refers to an approach in which humans are actively involved in the AI development process, particularly in the design, training, and evaluation stages. This involvement of humans helps in identifying unproductive layers of the AI architecture, leading to the creation of lightweight AI architectures suitable for embedded applications. By enabling human-machine interaction (HMI) during AI inference, the AI-in-the-loop concept combines the strengths of AI and humans. In this approach, the AI primarily solves the task but can seek human assistance through appropriate HMI when unsure about the correctness of its inference. The use of HMI during AI development reduces the resources needed and makes AI more reliable and explainable. Additionally, the concept of human-in-the-loop is also applied in novelty generation, where humans provide guidance in selecting and filtering novelties generated by abstract models of environments.
What are ctcf mediated loops?3 answersCTCF-mediated loops are chromatin loops that are formed and regulated by the CCCTC-binding factor (CTCF) protein. CTCF is a transcription regulator involved in various cellular processes and plays a crucial role in three-dimensional (3D) genome organization. These loops are formed when CTCF binds to specific DNA sequences called CTCF binding sites (CBSs) and acts as an anchor for the cohesin protein complex. The cohesin complex extrudes DNA loops that are anchored by CTCF in a polar orientation. The orientation of CTCF binding polarity controls cohesin-mediated DNA looping, and the presence of CTCF motifs and sequence conservation are important indicators of chromatin loops. CTCF-mediated loops have been found to be involved in gene regulation, cell development, disease progression, and the spatial organization of the genome.
What is the phenomenon of human-in-the-loop training of neural network?5 answersHuman-in-the-loop training of neural networks refers to a training approach that involves the active participation of humans in the training process. In this approach, humans provide feedback or guidance to the neural network during the training phase, which helps improve the performance and interpretability of the model. The feedback can be in the form of labeled data, annotations, or evaluations of the model's output. By incorporating human feedback, the neural network can learn from the expertise and knowledge of humans, leading to better results. This approach has been applied in various domains such as music generation, polyp detection in colonoscopy, synthetic aperture radar target recognition, and feature aggregation in machine learning.
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