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Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements

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Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements

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Graph generative deep learning models with an application to circuit topologies

Room-Temperature Quantum Optomechanics and Free-Electron Quantum Optics

Quadruped-Frog: Rapid Online Optimization of Continuous Quadruped Jumping

Optical data processing using structural non-linearity

Euclid: The search for primordial features

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

CloudProphet: A Machine Learning-Based Performance Prediction for Public Clouds

Euclid: Validation of the MontePython forecasting tools

Million-scale data integrated deep neural network for phonon properties of heuslers spanning the periodic table

Generalization and Personalization of Machine Learning for Multimodal Mobile Sensing in Everyday Life

Correlation properties of a one-dimensional repulsive Bose gas at finite temperature

Plasmonic effects in the neutralization of slow ions at a metallic surface

Machine learning-aided generative molecular design

Towards a multiscale point cloud structural similarity metric

Data-Intensive Exploration of the Photoelectrochemical Responses of Main-Group Metal Sulfides

Meta-learning to address diverse Earth observation problems across resolutions

Saliency prediction in 360° architectural scenes: Performance and impact of daylight variations

Decoding the brain: From neural representations to mechanistic models

Robustness of Local Predictions in Atomistic Machine Learning Models