research
∙
05/26/2023
Flow Matching for Scalable Simulation-Based Inference
Neural posterior estimation methods based on discrete normalizing flows ...
share
research
∙
05/24/2023
Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation
Simulation-based inference (SBI) enables amortized Bayesian inference fo...
research
∙
05/24/2023
Simultaneous identification of models and parameters of scientific simulators
Many scientific models are composed of multiple discrete components, and...
research
∙
05/24/2023
Adversarial robustness of amortized Bayesian inference
Bayesian inference usually requires running potentially costly inference...
research
∙
01/09/2023
Multiscale Metamorphic VAE for 3D Brain MRI Synthesis
Generative modeling of 3D brain MRIs presents difficulties in achieving ...
research
∙
11/16/2022
Adapting to noise distribution shifts in flow-based gravitational-wave inference
Deep learning techniques for gravitational-wave parameter estimation hav...
research
∙
10/11/2022
Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference
We combine amortized neural posterior estimation with importance samplin...
research
∙
10/10/2022
Truncated proposals for scalable and hassle-free simulation-based inference
Simulation-based inference (SBI) solves statistical inverse problems by ...
research
∙
03/12/2022
GATSBI: Generative Adversarial Training for Simulation-Based Inference
Simulation-based inference (SBI) refers to statistical inference on stoc...
research
∙
03/08/2022
Variational methods for simulation-based inference
We present Sequential Neural Variational Inference (SNVI), an approach t...
research
∙
11/25/2021
Group equivariant neural posterior estimation
Simulation-based inference with conditional neural density estimators is...
research
∙
06/23/2021
Real-time gravitational-wave science with neural posterior estimation
We demonstrate unprecedented accuracy for rapid gravitational-wave param...
research
∙
01/12/2021
Benchmarking Simulation-Based Inference
Recent advances in probabilistic modelling have led to a large number of...
research
∙
07/17/2020
SBI – A toolkit for simulation-based inference
Scientists and engineers employ stochastic numerical simulators to model...
research
∙
10/03/2019
Inference of a mesoscopic population model from population spike trains
To understand how rich dynamics emerge in neural populations, we require...
research
∙
06/27/2019
Teaching deep neural networks to localize sources in super-resolution microscopy by combining simulation-based learning and unsupervised learning
Single-molecule localization microscopy constructs super-resolution imag...
research
∙
05/29/2019
Intrinsic dimension of data representations in deep neural networks
Deep neural networks progressively transform their inputs across multipl...
research
∙
05/17/2019
Automatic Posterior Transformation for Likelihood-Free Inference
How can one perform Bayesian inference on stochastic simulators with int...
research
∙
10/31/2018
Analyzing biological and artificial neural networks: challenges with opportunities for synergy?
Deep neural networks (DNNs) transform stimuli across multiple processing...
research
∙
05/23/2018
Likelihood-free inference with emulator networks
Approximate Bayesian Computation (ABC) provides methods for Bayesian inf...
research
∙
11/06/2017
Flexible statistical inference for mechanistic models of neural dynamics
Mechanistic models of single-neuron dynamics have been extensively studi...
research
∙
11/06/2017
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations
A powerful approach for understanding neural population dynamics is to e...
research
∙
11/06/2017
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders
Calcium imaging permits optical measurement of neural activity. Since in...
research
∙
10/12/2014