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Found 7 papers, 3 papers with code
Bayesian Conditional Diffusion Models for Versatile Spatiotemporal Turbulence Generation
A notable feature of our approach is the method proposed for long-span flow sequence generation, which is based on autoregressive gradient-based conditional sampling, eliminating the need for cumbersome retraining processes.
Evaluating General-Purpose AI with Psychometrics
Comprehensive and accurate evaluation of general-purpose AI systems such as large language models allows for effective mitigation of their risks and deepened understanding of their capabilities.
Personality Traits in Large Language Models
The advent of large language models (LLMs) has revolutionized natural language processing, enabling the generation of coherent and contextually relevant human-like text.
Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty
The equation residuals are used to inform the spline learning in a Bayesian manner, where approximate Bayesian uncertainty calibration techniques are employed to approximate posterior distributions of the trainable parameters.
Mental health concerns prelude the Great Resignation: Evidence from Social Media
To study the causes of the 2021 Great Resignation, we use text analysis to investigate the changes in work- and quit-related posts between 2018 and 2021 on Reddit.
An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions
This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data
In many applications, flow measurements are usually sparse and possibly noisy.
Computational Physics Data Analysis, Statistics and Probability Fluid Dynamics
