Search Results for author:
Found 7 papers, 5 papers with code
What does the sea say to the shore? A BERT based DST style approach for speaker to dialogue attribution in novels
We present a complete pipeline to extract characters in a novel and link them to their direct-speech utterances.
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.
Cosmological Field Emulation and Parameter Inference with Diffusion Models
Cosmological simulations play a crucial role in elucidating the effect of physical parameters on the statistics of fields and on constraining parameters given information on density fields.
A point cloud approach to generative modeling for galaxy surveys at the field level
We introduce a diffusion-based generative model to describe the distribution of galaxies in our Universe directly as a collection of points in 3-D space (coordinates) optionally with associated attributes (e. g., velocities and masses), without resorting to binning or voxelization.
Probabilistic reconstruction of Dark Matter fields from biased tracers using diffusion models
Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter components that cannot be directly observed.
Simulation-based Inference for Exoplanet Atmospheric Retrieval: Insights from winning the Ariel Data Challenge 2023 using Normalizing Flows
These advancements pave the way for more effective and timely analysis of exoplanet atmospheric properties, advancing our understanding of these distant worlds.
XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasets
X-Ray image enhancement, along with many other medical image processing applications, requires the segmentation of images into bone, soft tissue, and open beam regions.
