8 citations · 18 across the 7 of their papers we have counts for
7 papers
Artificial to Spiking Neural Networks Conversion for Scientific Machine Learning
Qian Zhang, Chenxi Wu, Adar Kahana +4
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 ha…
FinVis-GPT: A Multimodal Large Language Model for Financial Chart Analysis
Ziao Wang, Yuhang Li, Junda Wu +2
In this paper, we propose FinVis-GPT, a novel multimodal large language model (LLM) specifically designed for financial chart analysis. By leveraging the power of LLMs and incorpor…
Diffusion Models for Probabilistic Deconvolution of Galaxy Images
Zhiwei Xue, Yuhang Li, Yash Patel +1
Telescopes capture images with a particular point spread function (PSF). Inferring what an image would have looked like with a much sharper PSF, a problem known as PSF deconvolutio…
Input-Aware Dynamic Timestep Spiking Neural Networks for Efficient In-Memory Computing
Yuhang Li, Abhishek Moitra, Tamar Geller +1
Spiking Neural Networks (SNNs) have recently attracted widespread research interest as an efficient alternative to traditional Artificial Neural Networks (ANNs) because of their ca…
Sharing Leaky-Integrate-and-Fire Neurons for Memory-Efficient Spiking Neural Networks
Youngeun Kim, Yuhang Li, Abhishek Moitra +2
Spiking Neural Networks (SNNs) have gained increasing attention as energy-efficient neural networks owing to their binary and asynchronous computation. However, their non-linear ac…
Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradient
Yuhang Li, Youngeun Kim, Hyoungseob Park +1
Spiking Neural Networks (SNNs) are recognized as the candidate for the next-generation neural networks due to their bio-plausibility and energy efficiency. Recently, researchers ha…