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Found 38 papers, 20 papers with code
All-in-one simulation-based inference
Amortized Bayesian inference trains neural networks to solve stochastic inference problems using model simulations, thereby making it possible to rapidly perform Bayesian inference for any newly observed data.
Diffusion Tempering Improves Parameter Estimation with Probabilistic Integrators for Ordinary Differential Equations
Ordinary differential equations (ODEs) are widely used to describe dynamical systems in science, but identifying parameters that explain experimental measurements is challenging.
Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation
Scientific modeling applications often require estimating a distribution of parameters consistent with a dataset of observations - an inference task also known as source distribution estimation.
Amortized Bayesian Decision Making for simulation-based models
Simulation-based inference (SBI) provides a powerful framework for inferring posterior distributions of stochastic simulators in a wide range of domains.
Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice Shelf from Isochronal Layers
The geometry of ice shelves, and hence their buttressing strength, is determined by ice flow as well as by the local surface accumulation and basal melt rates, governed by atmospheric and oceanic conditions.
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.
Generalized Bayesian Inference for Scientific Simulators via Amortized Cost Estimation
Generalized Bayesian Inference (GBI) aims to robustify inference for (misspecified) simulator models, replacing the likelihood-function with a cost function that evaluates the goodness of parameters relative to data.
Adversarial robustness of amortized Bayesian inference
Bayesian inference usually requires running potentially costly inference procedures separately for every new observation.
Simultaneous identification of models and parameters of scientific simulators
We approach this problem in an amortized simulation-based inference framework: We define implicit model priors over a fixed set of candidate components and train neural networks to infer joint probability distributions over both, model components and associated parameters from simulations.
Multiscale Metamorphic VAE for 3D Brain MRI Synthesis
Generative modeling of 3D brain MRIs presents difficulties in achieving high visual fidelity while ensuring sufficient coverage of the data distribution.
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.
GATSBI: Generative Adversarial Training for Simulation-Based Inference
We study the relationship between SBI and GANs, and introduce GATSBI, an adversarial approach to SBI.
Variational methods for simulation-based inference
We present Sequential Neural Variational Inference (SNVI), an approach to perform Bayesian inference in models with intractable likelihoods.
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.
Benchmarking Simulation-Based Inference
We set out to fill this gap: We provide a benchmark with inference tasks and suitable performance metrics, with an initial selection of algorithms including recent approaches employing neural networks and classical Approximate Bayesian Computation methods.
SBI -- A toolkit for simulation-based inference
$\texttt{sbi}$ facilitates inference on black-box simulators for practising scientists and engineers by providing a unified interface to state-of-the-art algorithms together with documentation and tutorials.
Inference of a mesoscopic population model from population spike trains
We derive the likelihood of both single-neuron and connectivity parameters given this activity, which can then be used to either optimize parameters by gradient ascent on the log-likelihood, or to perform Bayesian inference using Markov Chain Monte Carlo (MCMC) sampling.
Teaching deep neural networks to localize single molecules for super-resolution microscopy
We present a novel localization algorithm based on deep learning which significantly improves upon the state of the art.
Intrinsic dimension of data representations in deep neural networks
We find that, in a trained network, the ID is orders of magnitude smaller than the number of units in each layer.
Automatic Posterior Transformation for Likelihood-Free Inference
How can one perform Bayesian inference on stochastic simulators with intractable likelihoods?
Analyzing biological and artificial neural networks: challenges with opportunities for synergy?
We explore opportunities for synergy between the two fields, such as the use of DNNs as in-silico model systems for neuroscience, and how this synergy can lead to new hypotheses about the operating principles of biological neural networks.
Likelihood-free inference with emulator networks
Approximate Bayesian Computation (ABC) provides methods for Bayesian inference in simulation-based stochastic models which do not permit tractable likelihoods.
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations
Current approaches for dimensionality reduction on neural data are limited to single population recordings, and can not identify dynamics embedded across multiple measurements.
Fast amortized inference of neural activity from calcium imaging data with variational autoencoders
Calcium imaging permits optical measurement of neural activity.
Flexible statistical inference for mechanistic models of neural dynamics
Our approach builds on recent advances in ABC by learning a neural network which maps features of the observed data to the posterior distribution over parameters.
Signatures of criticality arise in simple neural population models with correlations
Support for this notion has come from a series of studies which identified statistical signatures of criticality in the ensemble activity of retinal ganglion cells.
Neurons and Cognition
Unlocking neural population non-stationarities using hierarchical dynamics models
Neural population activity often exhibits rich variability.
Low-dimensional models of neural population activity in sensory cortical circuits
Moreover, because the nonlinear stimulus inputs are mixed by the ongoing dynamics, the model can account for a relatively large number of idiosyncratic receptive field shapes with a small number of nonlinear inputs to a low-dimensional latent dynamical model.
A Bayesian model for identifying hierarchically organised states in neural population activity
Here, we present a statistical model for extracting hierarchically organised neural population states from multi-channel recordings of neural spiking activity.
Hierarchical models for neural population dynamics in the presence of non-stationarity
Here, we introduce a hierarchical statistical model of neural population activity which models both neural population dynamics as well as inter-trial modulations in firing rates.
Inferring neural population dynamics from multiple partial recordings of the same neural circuit
Simultaneous recordings of the activity of large neural populations are extremely valuable as they can be used to infer the dynamics and interactions of neurons in a local circuit, shedding light on the computations performed.
Spectral learning of linear dynamics from generalised-linear observations with application to neural population data
Here, we show how spectral learning methods for linear systems with Gaussian observations (usually called subspace identification in this context) can be extended to estimate the parameters of dynamical system models observed through non-Gaussian noise models.
Empirical models of spiking in neural populations
Neurons in the neocortex code and compute as part of a locally interconnected population.
How biased are maximum entropy models?
However, maximum entropy models fit to small data sets can be subject to sampling bias; i. e. the true entropy of the data can be severely underestimated.
Bayesian estimation of orientation preference maps
Imaging techniques such as optical imaging of intrinsic signals, 2-photon calcium imaging and voltage sensitive dye imaging can be used to measure the functional organization of visual cortex across different spatial scales.
Receptive Fields without Spike-Triggering
Can we find a concise description for the processing of a whole population of neurons analogous to the receptive field for single neurons?
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