Peter Maass

About Peter Maass

Peter Maass, With an exceptional h-index of 43 and a recent h-index of 28 (since 2020), a distinguished researcher at Universität Bremen, specializes in the field of mathematics, scientific computing.

His recent articles reflect a diverse array of research interests and contributions to the field:

Smooth Deep Saliency

How GAN Generators can Invert Networks in Real-Time

Svd-dip: Overcoming the overfitting problem in dip-based ct reconstruction

Deep learning based histological classification of adnex tumors

Parameter identification by deep learning of a material model for granular media

Deep learning detection of melanoma metastases in lymph nodes

PatchNR: learning from very few images by patch normalizing flow regularization

Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time

Peter Maass Information

University

Position

Professor Mathematics

Citations(all)

6949

Citations(since 2020)

3031

Cited By

4980

hIndex(all)

43

hIndex(since 2020)

28

i10Index(all)

105

i10Index(since 2020)

66

Email

University Profile Page

Google Scholar

Peter Maass Skills & Research Interests

mathematics

scientific computing

Top articles of Peter Maass

Smooth Deep Saliency

arXiv preprint arXiv:2404.02282

2024/4/2

How GAN Generators can Invert Networks in Real-Time

2024/2/27

Svd-dip: Overcoming the overfitting problem in dip-based ct reconstruction

2024/1/23

Deep learning based histological classification of adnex tumors

European Journal of Cancer

2024/1/1

Parameter identification by deep learning of a material model for granular media

arXiv preprint arXiv:2307.04166

2023/7/9

Peter Maass
Peter Maass

H-Index: 26

Deep learning detection of melanoma metastases in lymph nodes

European Journal of Cancer

2023/7/1

PatchNR: learning from very few images by patch normalizing flow regularization

Inverse Problems

2023/5/16

Peter Maass
Peter Maass

H-Index: 26

Model Stitching and Visualization How GAN Generators can Invert Networks in Real-Time

arXiv preprint arXiv:2302.02181

2023/2/4

Neural representation of the stratospheric ozone chemistry

Environmental Data Science

2023/1

Peter Maass
Peter Maass

H-Index: 26

Invertible residual networks in the context of regularization theory for linear inverse problems

Inverse Problems

2023/11/13

Tobias Kluth
Tobias Kluth

H-Index: 10

Peter Maass
Peter Maass

H-Index: 26

Einsatz künstlicher Intelligenz mittels Deep Learning in der dermatopathologischen Routinediagnostik des Basalzellkarzinoms: Applying an artificial intelligence …

JDDG: Journal der Deutschen Dermatologischen Gesellschaft

2023/11

Applying an artificial intelligence deep learning approach to routine dermatopathological diagnosis of basal cell carcinoma

JDDG: Journal der Deutschen Dermatologischen Gesellschaft

2023/11

Electrical Impedance Tomography: A Fair Comparative Study on Deep Learning and Analytic-based Approaches

arXiv preprint arXiv:2310.18636

2023/10/28

Steerable conditional diffusion for out-of-distribution adaptation in imaging inverse problems

arXiv preprint arXiv:2308.14409

2023/8/28

Score-based generative models for PET image reconstruction

arXiv preprint arXiv:2308.14190

2023/8/27

Deep learning methods for partial differential equations and related parameter identification problems

arXiv e-prints

2022/12

Peter Maass
Peter Maass

H-Index: 26

DL4TO : A Deep Learning Library for Sample-Efficient Topology Optimization

2023/8/1

An educated warm start for deep image prior-based micro CT reconstruction

IEEE Transactions on Computational Imaging

2022/12/30

Multimodal Lung Cancer Subtyping Using Deep Learning Neural Networks on Whole Slide Tissue Images and MALDI MSI

Cancers

2022/12/14

Selto: Sample-efficient learned topology optimization

arXiv preprint arXiv:2209.05098

2022/9/12

Peter Maass
Peter Maass

H-Index: 26

See List of Professors in Peter Maass University(Universität Bremen)