Search Results for author:
Found 5 papers, 3 papers with code
LtU-ILI: An All-in-One Framework for Implicit Inference in Astrophysics and Cosmology
This paper presents the Learning the Universe Implicit Likelihood Inference (LtU-ILI) pipeline, a codebase for rapid, user-friendly, and cutting-edge machine learning (ML) inference in astrophysics and cosmology.
Information-Ordered Bottlenecks for Adaptive Semantic Compression
We present the information-ordered bottleneck (IOB), a neural layer designed to adaptively compress data into latent variables ordered by likelihood maximization.
WikiWhy: Answering and Explaining Cause-and-Effect Questions
As large language models (LLMs) grow larger and more sophisticated, assessing their "reasoning" capabilities in natural language grows more challenging.
Approximate Bayesian Uncertainties on Deep Learning Dynamical Mass Estimates of Galaxy Clusters
We study methods for reconstructing Bayesian uncertainties on dynamical mass estimates of galaxy clusters using convolutional neural networks (CNNs).
Cosmology and Nongalactic Astrophysics
A Robust and Efficient Deep Learning Method for Dynamical Mass Measurements of Galaxy Clusters
Our first model, CNN$_\text{1D}$, infers cluster mass directly from the distribution of member galaxy line-of-sight velocities.
Cosmology and Nongalactic Astrophysics
