Paris workshop on Bayesian Deep Learning for Cosmology and Time Domain Astrophysics

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Sessions*

Lightning talks

Organisers

Contribution talks

Tutorial

Break

Breakout sessions

Tutorials

Uncertainties

Time Domain Astrophysics

ML Methodology

Poster session

Cosmology

Ethics of large scale machine learning

Cosmology

Gravitational Waves

Conclusion & discussion session

ML Tools

Round table

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Contributions*

A Bayesian Convolutional Neural Network for Robust Galaxy Ellipticity Regression (dans la session "Lightning talks")

A Convolutional Neural Network Approach to Classifying Supernova Time-Series Photometry (dans la session "Time Domain Astrophysics")

Adapting to big data: active anomaly detection on ZTF (dans la session "Time Domain Astrophysics")

Amortized variational inference for supernovae light curves (dans la session "ML Methodology")

Applying Likelihood-Free Inference to LISA parameter estimation: a project (dans la session "Lightning talks")

Artificial Intelligence: a game-changer for large scale structure cosmology (dans la session "Cosmology")

Background Estimation in Fermi Gamma-ray Burst Monitor lightcurves through a Neural Network (dans la session "Lightning talks")

Bayesian DL methods in extragalactic, time-domain studies (dans la session "Time Domain Astrophysics")

Bayesian planetary numerical ephemerides B-INPOP with MCMC (dans la session "Lightning talks")

Bayesian Time Series Analysis with TFP on JAX (dans la session "Tutorials")

Breakout session over lunch

Complete inference for binary-black hole gravitational wave data analysis (dans la session "Lightning talks")

COSMOPOWER: Deep Learning - accelerated Bayesian inference from next-generation cosmological surveys (dans la session "Cosmology")

Deblending Galaxies with Generative Adversarial Networks (dans la session "Cosmology")

Deep Learning Techniques for Time Series Analysis in the context of Gravitational Waves Detection (dans la session "Lightning talks")

Enhancing gravitational-wave population inference with deep learning (dans la session "ML Methodology")

Fast Inference of Star Formation Histories of Galaxies with Spectra and Simulation-Based Inference (SBI) (dans la session "ML Methodology")

Fast Parameter Estimation for Massive Black Hole Binaries with Normalising Flows (dans la session "Gravitational Waves")

Galaxies and Halos on Graph Neural Networks: Deep Generative Modeling Scalar and Vector Quantities for Intrinsic Alignment (dans la session "Cosmology")

Goodness-of-fit Evaluation: Toward Trustworthy Probabilistic Machine Learning (dans la session "Ethics of large scale machine learning")

Graph-Convolutional Neural Networks for large-scale structure clustering (dans la session "Cosmology")

HARMONIC: Bayesian model comparison for simulation-based inference (dans la session "ML Methodology")

Hybrid Physical-Neural ODEs for fast differentiable N-body Simulations (dans la session "Cosmology")

Improvement of the statistical model of a photoz bayesian estimator hybridizing a SED template fitting and a gaussian process (dans la session "Lightning talks")

Inferring Cosmological parameters using Normalizing Flows and Gravitational Waves (CosmoFlow) (dans la session "Lightning talks")

Interpreting non-Gaussian posterior distributions of cosmological parameters with normalizing flows (dans la session "Cosmology")

Introduction to Bayesian Modeling (dans la session "Tutorials")

Keynote (dans la session "Ethics of large scale machine learning")

Likelihood-Free Inference and Application to Cosmology (dans la session "Tutorials")

Maximum-A-posteriori estimate with Deep generative NEtworks for Source Separation (MADNESS) (dans la session "Cosmology")

ML in the next decades (dans la session "Round table")

ML inference & uncertainties (dans la session "Round table")

Neural Posterior Estimation with Differentiable Simulators (dans la session "ML Methodology")

Probabilistic Deep Learning with TFP and JAX/Flax (dans la session "Tutorials")

Sampling high-dimensional posterior with a simulation based prior (dans la session "Uncertainties")

Scalable Bayesian Workflows in JAX (dans la session "ML Methodology")

Search for ultra-fast radio bursts (dans la session "Lightning talks")

Searching for changing-state AGNs in massive datasets with anomaly detection (dans la session "Time Domain Astrophysics")

Self-Supervised Machine Learning; Detecting Galaxy Tidal Features in HSC-SSP (dans la session "ML Methodology")

Simulation-based inference for gravitational waves (dans la session "Gravitational Waves")

SNAD miner: Finding Missed Transient Events in ZTF DR4 (dans la session "Lightning talks")

SNAD: anomaly detection for large scale time-domain astronomy (dans la session "Time Domain Astrophysics")

The Extended LSST Astronomical Time Series Classification Challenge (ELAsTiCC) (dans la session "Cosmology")

There's no difference: Convolutional Neural Networks for transient detection without template subtraction (dans la session "Lightning talks")

Unraveling the role of cosmic velocity field in dark matter halo mass function using deep learning (dans la session "Cosmology")

When (not) to use uncertainties for ML in particle physics (dans la session "Uncertainties")

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Les événements programmés le*

20/06/2022

21/06/2022

22/06/2022

23/06/2022

24/06/2022

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