Search Results for author: Cornelius Schröder

Found 6 papers, 5 papers with code

Sourcerer: Sample-based Maximum Entropy Source Distribution Estimation

1 code implementation12 Feb 2024 Julius Vetter, Guy Moss, Cornelius Schröder, Richard Gao, Jakob H. Macke

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.

valid

Simulation-Based Inference of Surface Accumulation and Basal Melt Rates of an Antarctic Ice Shelf from Isochronal Layers

2 code implementations3 Dec 2023 Guy Moss, Vjeran Višnjević, Olaf Eisen, Falk M. Oraschewski, Cornelius Schröder, Jakob H. Macke, Reinhard Drews

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.

Bayesian Inference

Simultaneous identification of models and parameters of scientific simulators

no code implementations24 May 2023 Cornelius Schröder, Jakob H. Macke

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.

Bayesian Inference

System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina

1 code implementation NeurIPS 2020 Cornelius Schröder, David Klindt, Sarah Strauss, Katrin Franke, Matthias Bethge, Thomas Euler, Philipp Berens

Here, we present a computational model of temporal processing in the inner retina, including inhibitory feedback circuits and realistic synaptic release mechanisms.

Blocking

Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse

1 code implementation NeurIPS 2019 Cornelius Schröder, Ben James, Leon Lagnado, Philipp Berens

The inherent noise of neural systems makes it difficult to construct models which accurately capture experimental measurements of their activity.

Bayesian Inference Descriptive

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