Prospects for Quantum Equivariant Neural Networks by Castelazo, Grecia

“…Convolutional neural networks (CNNs) exploit translational invariance within images. …”
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On Neural Network Pruning’s Effect on Generalization by Jin, Tian

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On the Expressiveness and Generalization of Hypergraph Neural Networks by Luo, Zhezheng

“…Graph Neural Networks have demonstrated their success on many applications, including analyzing molecules and social networks. …”
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Characterizations of how neural networks learn by Boix-Adsera, Enric

“…Training neural network architectures on Internet-scale datasets has led to many recent advances in machine learning. …”
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Automated Mechanistic Interpretability for Neural Networks by Liao, Isaac C.

“…This thesis details three novel methods for automating the interpretation process for neural networks that are too large to manually interpret. …”
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Assessment of the modeling abilities of neural networks by Ramsey, Alvin L. (Alvin Lee)

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Control applications using neural networks by Showalter, Barton Earl

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Learning dynamics in feedforward neural networks by Shah, Jagesh V. (Jagesh Vijaykumar)

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Organic LEDs for optoelectronic neural networks by Mars, Risha R

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Dynamics and learning in recurrent neural networks by Xie, Xiaohui, 1972-

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Neural network model for differential GPS by Low, Kwong Hwee.

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Demystifying adversarial attacks on neural networks by Yip, Lionell En Zhi

“…Prevalent use of Neural Networks for Classification Tasks has brought to attention the security and integrity of the Neural Networks that industries are so reliant on. …”
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Investigating vulnerability of watermarking neural network by Chua, Viroy Sheng Yang

“…A neural network with great performance often incurs a high cost to train. …”
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Scene parsing with deep neural networks by Ding, Henghui

“…We address scene parsing based on deep neural networks and explore to enhance scene parsing performance from different aspects. …”
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Graph neural network with knowledge graph by Ang, Qi Xuan

“…More recently, there has been works on investigating the use of Graph Convolutional Neural Network for learning the knowledge graph embeddings. …”
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Exciton-polariton multilayered neural networks by Go, Michael

“…Nonlinearity is essential for neural network models to solve high complexity tasks. …”
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Adaptive learning rate for neural network by Teo, Chee Seong

“…The learning rate is one of the most important hyper-parameters to tune in a neural network and Deep Learning. The right choice of learning rate results in a better model and faster convergence during the learning process. …”
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Visualisation of 3D neural network by Goh, Yong Wei

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Symmetry perception with spiking neural networks by George, Jonathan K., Soci, Cesare, Miscuglio, Mario, Sorger, Volker J.

“…Here, we show that the coincidence detection property of a spiking-based feed-forward neural network enables mirror symmetry. Testing this algorithm exemplary on geospatial satellite image data sets reveals how symmetry density enables automated recognition of man-made structures over vegetation. …”
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Parallel simulation of spiking neural networks by Tang, Marcus Zi Yang

“…Spiking neural networks transfer information through activation spikes that carry information through their weight and temporal delay. …”
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