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Found 12 papers, 9 papers with code
Physics-Guided Learning of Meteorological Dynamics for Weather Downscaling and Forecasting
Weather forecasting is essential but remains computationally intensive and physically incomplete in traditional numerical weather prediction (NWP) methods.
R&D-Agent: Automating Data-Driven AI Solution Building Through LLM-Powered Automated Research, Development, and Evolution
Recent advances in AI and ML have transformed data science, yet increasing complexity and expertise requirements continue to hinder progress.
Generating Full-field Evolution of Physical Dynamics from Irregular Sparse Observations
To fill the gaps, we present SDIFT, Sequential DIffusion in Functional Tucker space, a novel framework that generates full-field evolution of physical dynamics from irregular sparse observations.
Functional Complexity-adaptive Temporal Tensor Decomposition
While recent works on temporal tensor decomposition have made significant progress by incorporating continuous timestamps in latent factors, they still struggle with general tensor data with continuous indexes not only in the temporal mode but also in other modes, such as spatial coordinates in climate data.
MarS: a Financial Market Simulation Engine Powered by Generative Foundation Model
Generative models aim to simulate realistic effects of various actions across different contexts, from text generation to visual effects.
Diffusion-Generative Multi-Fidelity Learning for Physical Simulation
We propose a conditional score model to control the solution generation by the input parameters and the fidelity.
Functional Bayesian Tucker Decomposition for Continuous-indexed Tensor Data
To generalize Tucker decomposition to such scenarios, we propose Functional Bayesian Tucker Decomposition (FunBaT).
Solving High Frequency and Multi-Scale PDEs with Gaussian Processes
Machine learning based solvers have garnered much attention in physical simulation and scientific computing, with a prominent example, physics-informed neural networks (PINNs).
BayOTIDE: Bayesian Online Multivariate Time series Imputation with functional decomposition
More importantly, almost all methods assume the observations are sampled at regular time stamps, and fail to handle complex irregular sampled time series arising from different applications.
Provably Convergent Schrödinger Bridge with Applications to Probabilistic Time Series Imputation
We show that optimizing the transport cost improves the performance and the proposed algorithm achieves the state-of-the-art result in healthcare and environmental data while exhibiting the advantage of exploring both temporal and feature patterns in probabilistic time series imputation.
Analysis of Multivariate Scoring Functions for Automatic Unbiased Learning to Rank
Because click data is often noisy and biased, a variety of methods have been proposed to construct unbiased learning to rank (ULTR) algorithms for the learning of unbiased ranking models.
Streaming Probabilistic Deep Tensor Factorization
Our algorithm provides responsive incremental updates for the posterior of the latent factors and NN weights upon receiving new tensor entries, and meanwhile select and inhibit redundant/useless weights.
