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  1. AU="Kothe, Ullrich"
  2. AU="Bhosale, Santosh D"
  3. AU="Santamarina-Albertos, Alba"
  4. AU="Scott M. Riester"
  5. AU="A Zappa, Marco"
  6. AU=Panciani Pier Paolo
  7. AU="La Cascio, L"
  8. AU="Getsuwan, Songpon"

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  1. Artikel ; Online: Neural superstatistics for Bayesian estimation of dynamic cognitive models.

    Schumacher, Lukas / Bürkner, Paul-Christian / Voss, Andreas / Köthe, Ullrich / Radev, Stefan T

    Scientific reports

    2023  Band 13, Heft 1, Seite(n) 13778

    Abstract: Mathematical models of cognition are often memoryless and ignore potential fluctuations of their parameters. However, human cognition is inherently dynamic. Thus, we propose to augment mechanistic cognitive models with a temporal dimension and estimate ... ...

    Abstract Mathematical models of cognition are often memoryless and ignore potential fluctuations of their parameters. However, human cognition is inherently dynamic. Thus, we propose to augment mechanistic cognitive models with a temporal dimension and estimate the resulting dynamics from a superstatistics perspective. Such a model entails a hierarchy between a low-level observation model and a high-level transition model. The observation model describes the local behavior of a system, and the transition model specifies how the parameters of the observation model evolve over time. To overcome the estimation challenges resulting from the complexity of superstatistical models, we develop and validate a simulation-based deep learning method for Bayesian inference, which can recover both time-varying and time-invariant parameters. We first benchmark our method against two existing frameworks capable of estimating time-varying parameters. We then apply our method to fit a dynamic version of the diffusion decision model to long time series of human response times data. Our results show that the deep learning approach is very efficient in capturing the temporal dynamics of the model. Furthermore, we show that the erroneous assumption of static or homogeneous parameters will hide important temporal information.
    Sprache Englisch
    Erscheinungsdatum 2023-08-23
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-023-40278-3
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Buch ; Konferenzbeitrag: Applications of discrete geometry and mathematical morphology

    Köthe, Ullrich

    first international workshop, WADGMM 2010, Istanbul, Turkey, August 22, 2010 ; revised selected papers

    (Lecture notes in computer science ; 7346)

    2012  

    Körperschaft WADGMM
    Veranstaltung/Kongress WADGMM (1, 2010.08.22, IstanbulTurkey) ; Workshop on Applications of Discrete Geometry and Mathematical Morphology (1, 2010.08.22, IstanbulTurkey)
    Verfasserangabe Ullrich Köthe ... (eds.)
    Serientitel Lecture notes in computer science ; 7346
    Schlagwörter Computer vision/Mathematics ; Discrete geometry ; Image analysis/Mathematics ; Pattern recognition systems/Mathematics ; Mustererkennung ; Diskrete Geometrie ; Mathematische Morphologie
    Sprache Englisch
    Umfang VII, 167 S., Ill., graph. Darst., 235 mm x 155 mm
    Verlag Springer
    Erscheinungsort Heidelberg u.a.
    Dokumenttyp Buch ; Konferenzbeitrag
    Anmerkung Literaturangaben
    ISBN 364232312X ; 9783642323126 ; 9783642323133 ; 3642323138
    Datenquelle Katalog der Technische Informationsbibliothek Hannover

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  3. Buch ; Online: Learning Distributions on Manifolds with Free-form Flows

    Sorrenson, Peter / Draxler, Felix / Rousselot, Armand / Hummerich, Sander / Köthe, Ullrich

    2023  

    Abstract: Many real world data, particularly in the natural sciences and computer vision, lie on known Riemannian manifolds such as spheres, tori or the group of rotation matrices. The predominant approaches to learning a distribution on such a manifold require ... ...

    Abstract Many real world data, particularly in the natural sciences and computer vision, lie on known Riemannian manifolds such as spheres, tori or the group of rotation matrices. The predominant approaches to learning a distribution on such a manifold require solving a differential equation in order to sample from the model and evaluate densities. The resulting sampling times are slowed down by a high number of function evaluations. In this work, we propose an alternative approach which only requires a single function evaluation followed by a projection to the manifold. Training is achieved by an adaptation of the recently proposed free-form flow framework to Riemannian manifolds. The central idea is to estimate the gradient of the negative log-likelihood via a trace evaluated in the tangent space. We evaluate our method on various manifolds, and find significantly faster inference at competitive performance compared to previous work. We make our code public at https://github.com/vislearn/FFF.

    Comment: Preprint, under review
    Schlagwörter Computer Science - Machine Learning ; Statistics - Machine Learning
    Thema/Rubrik (Code) 514
    Erscheinungsdatum 2023-12-15
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  4. Buch ; Online: Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data

    Schmier, Robert / Köthe, Ullrich / Straehle, Christoph-Nikolas

    2022  

    Abstract: Detecting test data deviating from training data is a central problem for safe and robust machine learning. Likelihoods learned by a generative model, e.g., a normalizing flow via standard log-likelihood training, perform poorly as an outlier score. We ... ...

    Abstract Detecting test data deviating from training data is a central problem for safe and robust machine learning. Likelihoods learned by a generative model, e.g., a normalizing flow via standard log-likelihood training, perform poorly as an outlier score. We propose to use an unlabelled auxiliary dataset and a probabilistic outlier score for outlier detection. We use a self-supervised feature extractor trained on the auxiliary dataset and train a normalizing flow on the extracted features by maximizing the likelihood on in-distribution data and minimizing the likelihood on the contrastive dataset. We show that this is equivalent to learning the normalized positive difference between the in-distribution and the contrastive feature density. We conduct experiments on benchmark datasets and compare to the likelihood, the likelihood ratio and state-of-the-art anomaly detection methods.
    Schlagwörter Computer Science - Machine Learning
    Thema/Rubrik (Code) 310
    Erscheinungsdatum 2022-08-30
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    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  5. Artikel ; Online: Long short-term memory networks for proton dose calculation in highly heterogeneous tissues.

    Neishabouri, Ahmad / Wahl, Niklas / Mairani, Andrea / Köthe, Ullrich / Bangert, Mark

    Medical physics

    2021  Band 48, Heft 4, Seite(n) 1893–1908

    Abstract: Purpose: To investigate the feasibility and accuracy of proton dose calculations with artificial neural networks (ANNs) in challenging three-dimensional (3D) anatomies.: Methods: A novel proton dose calculation approach was designed based on the ... ...

    Abstract Purpose: To investigate the feasibility and accuracy of proton dose calculations with artificial neural networks (ANNs) in challenging three-dimensional (3D) anatomies.
    Methods: A novel proton dose calculation approach was designed based on the application of a long short-term memory (LSTM) network. It processes the 3D geometry as a sequence of two-dimensional (2D) computed tomography slices and outputs a corresponding sequence of 2D slices that forms the 3D dose distribution. The general accuracy of the approach is investigated in comparison to Monte Carlo reference simulations and pencil beam dose calculations. We consider both artificial phantom geometries and clinically realistic lung cases for three different pencil beam energies.
    Results: For artificial phantom cases, the trained LSTM model achieved a 98.57% γ-index pass rate ([1%, 3 mm]) in comparison to MC simulations for a pencil beam with initial energy 104.25 MeV. For a lung patient case, we observe pass rates of 98.56%, 97.74%, and 94.51% for an initial energy of 67.85, 104.25, and 134.68 MeV, respectively. Applying the LSTM dose calculation on patient cases that were fully excluded from the training process yields an average γ-index pass rate of 97.85%.
    Conclusions: LSTM networks are well suited for proton dose calculation tasks. Further research, especially regarding model generalization and computational performance in comparison to established dose calculation methods, is warranted.
    Mesh-Begriff(e) Algorithms ; Humans ; Memory, Short-Term ; Monte Carlo Method ; Phantoms, Imaging ; Proton Therapy ; Protons ; Radiotherapy Dosage ; Radiotherapy Planning, Computer-Assisted
    Chemische Substanzen Protons
    Sprache Englisch
    Erscheinungsdatum 2021-03-11
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 188780-4
    ISSN 2473-4209 ; 0094-2405
    ISSN (online) 2473-4209
    ISSN 0094-2405
    DOI 10.1002/mp.14658
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  6. Artikel ; Online: BayesFlow: Learning Complex Stochastic Models With Invertible Neural Networks.

    Radev, Stefan T / Mertens, Ulf K / Voss, Andreas / Ardizzone, Lynton / Kothe, Ullrich

    IEEE transactions on neural networks and learning systems

    2022  Band 33, Heft 4, Seite(n) 1452–1466

    Abstract: Estimating the parameters of mathematical models is a common problem in almost all branches of science. However, this problem can prove notably difficult when processes and model descriptions become increasingly complex and an explicit likelihood ... ...

    Abstract Estimating the parameters of mathematical models is a common problem in almost all branches of science. However, this problem can prove notably difficult when processes and model descriptions become increasingly complex and an explicit likelihood function is not available. With this work, we propose a novel method for globally amortized Bayesian inference based on invertible neural networks that we call BayesFlow. The method uses simulations to learn a global estimator for the probabilistic mapping from observed data to underlying model parameters. A neural network pretrained in this way can then, without additional training or optimization, infer full posteriors on arbitrarily many real data sets involving the same model family. In addition, our method incorporates a summary network trained to embed the observed data into maximally informative summary statistics. Learning summary statistics from data makes the method applicable to modeling scenarios where standard inference techniques with handcrafted summary statistics fail. We demonstrate the utility of BayesFlow on challenging intractable models from population dynamics, epidemiology, cognitive science, and ecology. We argue that BayesFlow provides a general framework for building amortized Bayesian parameter estimation machines for any forward model from which data can be simulated.
    Mesh-Begriff(e) Bayes Theorem ; Learning ; Neural Networks, Computer
    Sprache Englisch
    Erscheinungsdatum 2022-04-04
    Erscheinungsland United States
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2162-2388
    ISSN (online) 2162-2388
    DOI 10.1109/TNNLS.2020.3042395
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  7. Artikel ; Online: Amortized Bayesian Model Comparison With Evidential Deep Learning.

    Radev, Stefan T / D'Alessandro, Marco / Mertens, Ulf K / Voss, Andreas / Kothe, Ullrich / Burkner, Paul-Christian

    IEEE transactions on neural networks and learning systems

    2023  Band 34, Heft 8, Seite(n) 4903–4917

    Abstract: Comparing competing mathematical models of complex processes is a shared goal among many branches of science. The Bayesian probabilistic framework offers a principled way to perform model comparison and extract useful metrics for guiding decisions. ... ...

    Abstract Comparing competing mathematical models of complex processes is a shared goal among many branches of science. The Bayesian probabilistic framework offers a principled way to perform model comparison and extract useful metrics for guiding decisions. However, many interesting models are intractable with standard Bayesian methods, as they lack a closed-form likelihood function or the likelihood is computationally too expensive to evaluate. In this work, we propose a novel method for performing Bayesian model comparison using specialized deep learning architectures. Our method is purely simulation-based and circumvents the step of explicitly fitting all alternative models under consideration to each observed dataset. Moreover, it requires no hand-crafted summary statistics of the data and is designed to amortize the cost of simulation over multiple models, datasets, and dataset sizes. This makes the method especially effective in scenarios where model fit needs to be assessed for a large number of datasets, so that case-based inference is practically infeasible. Finally, we propose a novel way to measure epistemic uncertainty in model comparison problems. We demonstrate the utility of our method on toy examples and simulated data from nontrivial models from cognitive science and single-cell neuroscience. We show that our method achieves excellent results in terms of accuracy, calibration, and efficiency across the examples considered in this work. We argue that our framework can enhance and enrich model-based analysis and inference in many fields dealing with computational models of natural processes. We further argue that the proposed measure of epistemic uncertainty provides a unique proxy to quantify absolute evidence even in a framework which assumes that the true data-generating model is within a finite set of candidate models.
    Sprache Englisch
    Erscheinungsdatum 2023-08-04
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ISSN 2162-2388
    ISSN (online) 2162-2388
    DOI 10.1109/TNNLS.2021.3124052
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  8. Buch ; Online: Towards Multimodal Depth Estimation from Light Fields

    Leistner, Titus / Mackowiak, Radek / Ardizzone, Lynton / Köthe, Ullrich / Rother, Carsten

    2022  

    Abstract: Light field applications, especially light field rendering and depth estimation, developed rapidly in recent years. While state-of-the-art light field rendering methods handle semi-transparent and reflective objects well, depth estimation methods either ... ...

    Abstract Light field applications, especially light field rendering and depth estimation, developed rapidly in recent years. While state-of-the-art light field rendering methods handle semi-transparent and reflective objects well, depth estimation methods either ignore these cases altogether or only deliver a weak performance. We argue that this is due current methods only considering a single "true" depth, even when multiple objects at different depths contributed to the color of a single pixel. Based on the simple idea of outputting a posterior depth distribution instead of only a single estimate, we develop and explore several different deep-learning-based approaches to the problem. Additionally, we contribute the first "multimodal light field depth dataset" that contains the depths of all objects which contribute to the color of a pixel. This allows us to supervise the multimodal depth prediction and also validate all methods by measuring the KL divergence of the predicted posteriors. With our thorough analysis and novel dataset, we aim to start a new line of depth estimation research that overcomes some of the long-standing limitations of this field.
    Schlagwörter Computer Science - Computer Vision and Pattern Recognition
    Thema/Rubrik (Code) 004
    Erscheinungsdatum 2022-03-30
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    Dokumenttyp Buch ; Online
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  9. Buch ; Online: Content-Aware Differential Privacy with Conditional Invertible Neural Networks

    Tölle, Malte / Köthe, Ullrich / André, Florian / Meder, Benjamin / Engelhardt, Sandy

    2022  

    Abstract: Differential privacy (DP) has arisen as the gold standard in protecting an individual's privacy in datasets by adding calibrated noise to each data sample. While the application to categorical data is straightforward, its usability in the context of ... ...

    Abstract Differential privacy (DP) has arisen as the gold standard in protecting an individual's privacy in datasets by adding calibrated noise to each data sample. While the application to categorical data is straightforward, its usability in the context of images has been limited. Contrary to categorical data the meaning of an image is inherent in the spatial correlation of neighboring pixels making the simple application of noise infeasible. Invertible Neural Networks (INN) have shown excellent generative performance while still providing the ability to quantify the exact likelihood. Their principle is based on transforming a complicated distribution into a simple one e.g. an image into a spherical Gaussian. We hypothesize that adding noise to the latent space of an INN can enable differentially private image modification. Manipulation of the latent space leads to a modified image while preserving important details. Further, by conditioning the INN on meta-data provided with the dataset we aim at leaving dimensions important for downstream tasks like classification untouched while altering other parts that potentially contain identifying information. We term our method content-aware differential privacy (CADP). We conduct experiments on publicly available benchmarking datasets as well as dedicated medical ones. In addition, we show the generalizability of our method to categorical data. The source code is publicly available at https://github.com/Cardio-AI/CADP.

    Comment: Accepted at 3rd DeCaF Workshop (MICCAI22)
    Schlagwörter Computer Science - Cryptography and Security ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; J.3 I.4.0 J.3 I.2.6
    Thema/Rubrik (Code) 006
    Erscheinungsdatum 2022-07-29
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    Dokumenttyp Buch ; Online
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  10. Buch ; Online: Benchmarking Invertible Architectures on Inverse Problems

    Kruse, Jakob / Ardizzone, Lynton / Rother, Carsten / Köthe, Ullrich

    2021  

    Abstract: Recent work demonstrated that flow-based invertible neural networks are promising tools for solving ambiguous inverse problems. Following up on this, we investigate how ten invertible architectures and related models fare on two intuitive, low- ... ...

    Abstract Recent work demonstrated that flow-based invertible neural networks are promising tools for solving ambiguous inverse problems. Following up on this, we investigate how ten invertible architectures and related models fare on two intuitive, low-dimensional benchmark problems, obtaining the best results with coupling layers and simple autoencoders. We hope that our initial efforts inspire other researchers to evaluate their invertible architectures in the same setting and put forth additional benchmarks, so our evaluation may eventually grow into an official community challenge.
    Schlagwörter Computer Science - Machine Learning ; 68T01
    Erscheinungsdatum 2021-01-26
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    Dokumenttyp Buch ; Online
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