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Found 8 papers, 0 papers with code
Difference Learning for Air Quality Forecasting Transport Emulation
Due to a recent increase in extreme air quality events, both globally and locally in the United States, finer resolution air quality forecasting guidance is needed to effectively adapt to these events.
Neuro-Symbolic Bi-Directional Translation -- Deep Learning Explainability for Climate Tipping Point Research
In this work we propose a neuro-symbolic approach called Neuro-Symbolic Question-Answer Program Translator, or NS-QAPT, to address explainability and interpretability for deep learning climate simulation, applied to climate tipping point discovery.
Forecast-Aware Model Driven LSTM
In this work we describe a method that combines unsupervised learning and a forecast-aware bi-directional LSTM network to perform bias correction for operational air quality forecasting using AirNow station data for ozone and PM2. 5 in the continental US.
A Generative Adversarial Network for Climate Tipping Point Discovery (TIP-GAN)
We describe an adversarial game to explore the parameter space of these models, detect upcoming tipping points, and discover the drivers of tipping points.
Using Artificial Intelligence to aid Scientific Discovery of Climate Tipping Points
We describe how this methodology can be applied to the discovery of climate tipping points and, in particular, the collapse of the Atlantic Meridional Overturning Circulation (AMOC).
A Hybrid Quantum enabled RBM Advantage: Convolutional Autoencoders For Quantum Image Compression and Generative Learning
We formulated an MNIST classification problem using a deep convolutional neural network that used samples from a quantum RBM to train the MNIST classifier and compared the results with an MNIST classifier trained with the original MNIST training data set, as well as an MNIST classifier trained using classical RBM samples.
Ontology-Grounded Topic Modeling for Climate Science Research
In scientific disciplines where research findings have a strong impact on society, reducing the amount of time it takes to understand, synthesize and exploit the research is invaluable.
