1.2k citations · 2.5k across the 17 of their papers we have counts for
20 papers
Diffusion Models for High-Resolution Solar Forecasts
Yusuke Hatanaka, Yannik Glaser, Geoff Galgon +2
Forecasting future weather and climate is inherently difficult. Machine learning offers new approaches to increase the accuracy and computational efficiency of forecasts, but curre…
Deep learning for improved keV-scale recoil identification in high resolution gas time projection chambers
J. Schueler, M. Ghrear, S. E. Vahsen +2
Recoil-imaging gaseous time projection chambers (TPCs) with directional sensitivity are attractive for dark matter (DM) searches. Detectors capable of reconstructing 3D nuclear rec…
Tourbillon: a Physically Plausible Neural Architecture
Mohammadamin Tavakoli, Peter Sadowski, Pierre Baldi
In a physical neural system, backpropagation is faced with a number of obstacles including: the need for labeled data, the violation of the locality learning principle, the need fo…
Recovery of TESS Stellar Rotation Periods Using Deep Learning
Zachary R. Claytor, Jennifer L. van Saders, Joe Llama +3
We used a convolutional neural network to infer stellar rotation periods from a set of synthetic light curves simulated with realistic spot evolution patterns. We convolved these s…
Proxy-Based Prediction of Solar Extreme Ultraviolet Emission using Deep Learning
Anthony L. Pineci, Peter Sadowski, Eric Gaidos +1
High-energy radiation from the Sun governs the behavior of Earth's upper atmosphere and such radiation from any planet-hosting star can drive the long-term evolution of a planetary…
Sherpa: Robust Hyperparameter Optimization for Machine Learning
Lars Hertel, Julian Collado, Peter Sadowski +2
Sherpa is a hyperparameter optimization library for machine learning models. It is specifically designed for problems with computationally expensive, iterative function evaluations…