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Found 8 papers, 5 papers with code
The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning
Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows.
Contextual Counting: A Mechanistic Study of Transformers on a Quantitative Task
Transformers have revolutionized machine learning across diverse domains, yet understanding their behavior remains crucial, particularly in high-stakes applications.
Listening to the Noise: Blind Denoising with Gibbs Diffusion
Assuming arbitrary parametric Gaussian noise, we develop a Gibbs algorithm that alternates sampling steps from a conditional diffusion model trained to map the signal prior to the family of noise distributions, and a Monte Carlo sampler to infer the noise parameters.
Removing Dust from CMB Observations with Diffusion Models
We investigate diffusion-based modeling of the dust foreground and its interest for component separation.
Simulation-based stacking
Simulation-based inference has been popular for amortized Bayesian computation.
Multiple Physics Pretraining for Physical Surrogate Models
We introduce multiple physics pretraining (MPP), an autoregressive task-agnostic pretraining approach for physical surrogate modeling of spatiotemporal systems with transformers.
xVal: A Continuous Numerical Tokenization for Scientific Language Models
Due in part to their discontinuous and discrete default encodings for numbers, Large Language Models (LLMs) have not yet been commonly used to process numerically-dense scientific datasets.
Statistical Component Separation for Targeted Signal Recovery in Noisy Mixtures
In the case of 1), we show that our method better recovers the descriptors of the target data than a standard denoising method in most situations.
