Search Results for author:
Found 9 papers, 4 papers with code
Inferring Atmospheric Properties of Exoplanets with Flow Matching and Neural Importance Sampling
Atmospheric retrievals (AR) characterize exoplanets by estimating atmospheric parameters from observed light spectra, typically by framing the task as a Bayesian inference problem.
Flow Matching for Scalable Simulation-Based Inference
Neural posterior estimation methods based on discrete normalizing flows have become established tools for simulation-based inference (SBI), but scaling them to high-dimensional problems can be challenging.
Adapting to noise distribution shifts in flow-based gravitational-wave inference
Deep learning techniques for gravitational-wave parameter estimation have emerged as a fast alternative to standard samplers $\unicode{x2013}$ producing results of comparable accuracy.
Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference
This shows a median sample efficiency of $\approx 10\%$ (two orders-of-magnitude better than standard samplers) as well as a ten-fold reduction in the statistical uncertainty in the log evidence.
Group equivariant neural posterior estimation
We here describe an alternative method to incorporate equivariances under joint transformations of parameters and data.
Real-time gravitational-wave science with neural posterior estimation
We demonstrate unprecedented accuracy for rapid gravitational-wave parameter estimation with deep learning.
Explicitly Modeled Attention Maps for Image Classification
Self-attention networks have shown remarkable progress in computer vision tasks such as image classification.
DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision
Alternative unsupervised approaches rely on careful selection of multiple handcrafted saliency methods to generate noisy pseudo-ground-truth labels.
DeepUSPS: Deep Robust Unsupervised Saliency Prediction With Self-Supervision
Alternative unsupervised approaches rely on careful selection of multiple handcrafted saliency methods to generate noisy pseudo-ground-truth labels.
