9 citations · 34 across the 15 of their papers we have counts for
8 papers · 1 filter
Analysis of biologically plausible neuron models for regression with spiking neural networks
Mario De Florio, Adar Kahana, George Em Karniadakis
This paper explores the impact of biologically plausible neuron models on the performance of Spiking Neural Networks (SNNs) for regression tasks. While SNNs are widely recognized f…
Rethinking Skip Connections in Spiking Neural Networks with Time-To-First-Spike Coding
Youngeun Kim, Adar Kahana, Ruokai Yin +4
Time-To-First-Spike (TTFS) coding in Spiking Neural Networks (SNNs) offers significant advantages in terms of energy efficiency, closely mimicking the behavior of biological neuron…
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…
MyCrunchGPT: A chatGPT assisted framework for scientific machine learning
Varun Kumar, Leonard Gleyzer, Adar Kahana +2
Scientific Machine Learning (SciML) has advanced recently across many different areas in computational science and engineering. The objective is to integrate data and physics seaml…
Understanding the Efficacy of U-Net & Vision Transformer for Groundwater Numerical Modelling
Maria Luisa Taccari, Oded Ovadia, He Wang +3
This paper presents a comprehensive comparison of various machine learning models, namely U-Net, U-Net integrated with Vision Transformers (ViT), and Fourier Neural Operator (FNO),…
Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)
Oded Ovadia, Vivek Oommen, Adar Kahana +3
Extrapolation remains a grand challenge in deep neural networks across all application domains. We propose an operator learning method to solve time-dependent partial differential…
ViTO: Vision Transformer-Operator
Oded Ovadia, Adar Kahana, Panos Stinis +2
We combine vision transformers with operator learning to solve diverse inverse problems described by partial differential equations (PDEs). Our approach, named ViTO, combines a U-N…
MessageNet: Message Classification using Natural Language Processing and Meta-data
Adar Kahana, Oren Elisha
In this paper we propose a new Deep Learning (DL) approach for message classification. Our method is based on the state-of-the-art Natural Language Processing (NLP) building blocks…