[HTML][HTML] Deep generative models for detector signature simulation: A taxonomic review
B Hashemi, C Krause - Reviews in Physics, 2024 - Elsevier
In modern collider experiments, the quest to explore fundamental interactions between
elementary particles has reached unparalleled levels of precision. Signatures from particle …
elementary particles has reached unparalleled levels of precision. Signatures from particle …
Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
B Hashemi, C Krause - arXiv preprint arXiv:2312.09597, 2023 - arxiv.org
In modern collider experiments, the quest to explore fundamental interactions between
elementary particles has reached unparalleled levels of precision. Signatures from particle …
elementary particles has reached unparalleled levels of precision. Signatures from particle …
Truncated proposals for scalable and hassle-free simulation-based inference
M Deistler, PJ Goncalves… - Advances in Neural …, 2022 - proceedings.neurips.cc
Simulation-based inference (SBI) solves statistical inverse problems by repeatedly running a
stochastic simulator and inferring posterior distributions from model-simulations. To improve …
stochastic simulator and inferring posterior distributions from model-simulations. To improve …
Learning robust statistics for simulation-based inference under model misspecification
Simulation-based inference (SBI) methods such as approximate Bayesian computation
(ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating …
(ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating …
Contrastive neural ratio estimation
Likelihood-to-evidence ratio estimation is usually cast as either a binary (NRE-A) or a
multiclass (NRE-B) classification task. In contrast to the binary classification framework, the …
multiclass (NRE-B) classification task. In contrast to the binary classification framework, the …
Meta-learning families of plasticity rules in recurrent spiking networks using simulation-based inference
B Confavreux, P Ramesh… - Advances in …, 2023 - proceedings.neurips.cc
There is substantial experimental evidence that learning and memory-related behaviours
rely on local synaptic changes, but the search for distinct plasticity rules has been driven by …
rely on local synaptic changes, but the search for distinct plasticity rules has been driven by …
JANA: Jointly amortized neural approximation of complex Bayesian models
This work proposes “jointly amortized neural approximation”(JANA) of intractable likelihood
functions and posterior densities arising in Bayesian surrogate modeling and simulation …
functions and posterior densities arising in Bayesian surrogate modeling and simulation …
Simulation-based calibration checking for Bayesian computation: The choice of test quantities shapes sensitivity
Simulation-based calibration checking (SBC) is a practical method to validate
computationally-derived posterior distributions or their approximations. In this paper, we …
computationally-derived posterior distributions or their approximations. In this paper, we …
Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy
Probabilistic (Bayesian) modeling has experienced a surge of applications in almost all
quantitative sciences and industrial areas. This development is driven by a combination of …
quantitative sciences and industrial areas. This development is driven by a combination of …
Detecting model misspecification in amortized Bayesian inference with neural networks
Recent advances in probabilistic deep learning enable efficient amortized Bayesian
inference in settings where the likelihood function is only implicitly defined by a simulation …
inference in settings where the likelihood function is only implicitly defined by a simulation …