no code implementations • 5 Dec 2023 • Chenxi Wu, Alan John Varghese, Vivek Oommen, George Em Karniadakis
Herein, we consider 13 GPT-related papers across different scientific domains, reviewed by a human reviewer and SciSpace, a large language model, with the reviews evaluated by three distinct types of evaluators, namely GPT-3. 5, a crowd panel, and GPT-4.
no code implementations • 1 Oct 2023 • Ole Richter, Chenxi Wu, Adrian M. Whatley, German Köstinger, Carsten Nielsen, Ning Qiao, Giacomo Indiveri
With the remarkable progress that technology has made, the need for processing data near the sensors at the edge has increased dramatically.
no code implementations • 31 Aug 2023 • Qian Zhang, Chenxi Wu, Adar Kahana, Youngeun Kim, Yuhang Li, George Em Karniadakis, Priyadarshini Panda
We introduce a method to convert Physics-Informed Neural Networks (PINNs), commonly used in scientific machine learning, to Spiking Neural Networks (SNNs), which are expected to have higher energy efficiency compared to traditional Artificial Neural Networks (ANNs).
1 code implementation • 14 Mar 2023 • Uğurcan Çakal, Maryada, Chenxi Wu, Ilkay Ulusoy, Dylan R. Muir
Here we demonstrate a novel methodology for ofDine training and deployment of spiking neural networks (SNNs) to the mixed-signal neuromorphic processor DYNAP-SE2.
no code implementations • 8 Sep 2022 • J. Elisenda Grigsby, Kathryn Lindsey, Robert Meyerhoff, Chenxi Wu
It is well-known that the parameterized family of functions representable by fully-connected feedforward neural networks with ReLU activation function is precisely the class of piecewise linear functions with finitely many pieces.
2 code implementations • 21 Jul 2022 • Chenxi Wu, Min Zhu, Qinyang Tan, Yadhu Kartha, Lu Lu
Hence, we have considered a total of 10 different sampling methods, including six non-adaptive uniform sampling, uniform sampling with resampling, two proposed adaptive sampling, and an existing adaptive sampling.
no code implementations • 14 Apr 2021 • Chunhua Ye, Zhong Yin, Chenxi Wu, Xiayidai Abulaiti, Yixing Zhang, Zhenqi Sun, Jianhua Zhang
The combination of Frequency and entropy feature and CNN has the highest classification accuracy, which is 85. 34%.