5 citations · 19 across the 15 of their papers we have counts for
7 papers · 1 filter
sbi reloaded: a toolkit for simulation-based inference workflows
Jan Boelts, Michael Deistler, Manuel Gloeckler +30
Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a…
Compositional simulation-based inference for time series
Manuel Gloeckler, Shoji Toyota, Kenji Fukumizu +1
Amortized simulation-based inference (SBI) methods train neural networks on simulated data to perform Bayesian inference. While this strategy avoids the need for tractable likeliho…
Neural timescales from a computational perspective
Roxana Zeraati, Anna Levina, Jakob H. Macke +1
Neural activity fluctuates over a wide range of timescales within and across brain areas. Experimental observations suggest that diverse neural timescales reflect information in dy…
Latent Diffusion for Neural Spiking Data
Jaivardhan Kapoor, Auguste Schulz, Julius Vetter +3
Modern datasets in neuroscience enable unprecedented inquiries into the relationship between complex behaviors and the activity of many simultaneously recorded neurons. While laten…
Inferring stochastic low-rank recurrent neural networks from neural data
Matthijs Pals, A Erdem Sağtekin, Felix Pei +2
A central aim in computational neuroscience is to relate the activity of large populations of neurons to an underlying dynamical system. Models of these neural dynamics should idea…
All-in-one simulation-based inference
Manuel Gloeckler, Michael Deistler, Christian Weilbach +2
Amortized Bayesian inference trains neural networks to solve stochastic inference problems using model simulations, thereby making it possible to rapidly perform Bayesian inference…
A Practical Guide to Sample-based Statistical Distances for Evaluating Generative Models in Science
Sebastian Bischoff, Alana Darcher, Michael Deistler +18
Generative models are invaluable in many fields of science because of their ability to capture high-dimensional and complicated distributions, such as photo-realistic images, prote…