Stars
Simulation based inference using flow matching and diffusion models in jax
Code repository for Trajectory Flow Matching
A teaching and research repository for exploring generative latent flow matching
KENN - A Transformer Encoder for Gravitational Waves
PyTorch-implementations of Flow Models for toy data
AI-driven discovery of new Gravitational Wave Detectors
Frequency-domain BBH TDI-2.0 response template and basic analysis tools, as an example for the analysis of Taiji Data Challenge.
PyTorch implementation of Fractal Generative Models.
Conditional version of Auto-Regressive Diffusion Model (ARDM) presented in https://arxiv.org/abs/2110.02037 https://github.com/google-research/google-research/tree/master/autoregressive_diffusion
“FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching” FlowAR employs a simplest scale design and is compatible with any VAE.
Python code and Jupyter notebooks for our data analysis around developing generalizable and robust neural networks for HRTEM analysis
Variational autoencoders for the analysis of high-resolution transmission electron microscopy data
Machine learning pipeline for predicting exoplanet dispositions from the Kepler dataset. Includes data preprocessing, feature scaling, SMOTE for balancing, and hyperparameter optimization with Grid…
A crossmatching between exoplanets that were detected by Kepler-TESS and K2-TESS.
This project was conducted to create a Machine Learning classification model that helps identifying whether Kepler’s objects of interest are exoplanets or not.
A New Statistical Model of Star Speckles for Learning to Detect and Characterize Exoplanets in Direct Imaging Observations (CVPR 2025)
Fast & scalable MCMC for all your exoplanet needs!
Code for the paper "Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation".
Exoplanet Detection using Machine Learning and Artificial Neural Networks to classify stellar light curves, leveraging advanced preprocessing, PCA, and classification techniques for accurate identi…
Finding new worlds from Kepler/TESS data with PyTorch-- A fork of ExoNet from Ansdell et al. 2018
Implementation of Autoregressive Diffusion in Pytorch
Deep Probabilistic Imaging (DPI): Uncertainty Quantification and Multi-modal Solution Characterization for Computational Imaging
Cosmological parameter inference using normalizing flows and GW events
Neural network model for gravitational wave sky localization
Repository for the paper for fast parameter estimation of BNS GW signals.
This package implements various flow-based MCMC algorithms for statistical analyses and sampling.
A prototype time-domain simulator for the data of space-based gravitational wave detectors.