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Found 7 papers, 2 papers with code
GPT vs Human for Scientific Reviews: A Dual Source Review on Applications of ChatGPT in Science
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.
DYNAP-SE2: a scalable multi-core dynamic neuromorphic asynchronous spiking neural network processor
With the remarkable progress that technology has made, the need for processing data near the sensors at the edge has increased dramatically.
Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning
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).
Gradient-descent hardware-aware training and deployment for mixed-signal Neuromorphic processors
Here we demonstrate a novel methodology for ofDine training and deployment of spiking neural networks (SNNs) to the mixed-signal neuromorphic processor DYNAP-SE2.
Functional dimension of feedforward ReLU neural networks
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.
A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks
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.
Identification of mental fatigue in language comprehension tasks based on EEG and deep learning
The combination of Frequency and entropy feature and CNN has the highest classification accuracy, which is 85. 34%.
