On Expressivity and Trainability of Quadratic Networks-Reference-Cited by-同舟云学术

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On Expressivity and Trainability of Quadratic Networks

Published:2023 Issue: Volume: Page:1-15
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Fan Feng-Lei¹^ORCID,Li Mengzhou²^ORCID,Wang Fei¹^ORCID,Lai Rongjie³^ORCID,Wang Ge²^ORCID

Affiliation:

1. Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, USA

2. Biomedical Imaging Center, AI-Based X-ray Imaging System (AXIS) Laboratory, Rensselaer Polytechnic Institute, Troy, NY, USA

3. Department of Mathematics, Rensselaer Polytechnic Institute, Troy, NY, USA

Funder

NSF Career

funding

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/10327752.pdf?arnumber=10327752

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2. Parallel proportional fusion of a spiking quantum neural network for optimizing image classification;Applied Intelligence;2024-09-03

3. Quadratic Neuron-Empowered Heterogeneous Autoencoder for Unsupervised Anomaly Detection;IEEE Transactions on Artificial Intelligence;2024-09

4. HQC-MCDCNN: a novel hybrid quantum–classical multi-path denoising convolutional neural network;Physica Scripta;2024-07-22

5. Toward Efficient and Interpretative Rolling Bearing Fault Diagnosis via Quadratic Neural Network With Bi-LSTM;IEEE Internet of Things Journal;2024-07-01

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