Patrick Thiam

Patrick Thiam

Universität Ulm

H-index: 16

Europe-Germany

About Patrick Thiam

Patrick Thiam, With an exceptional h-index of 16 and a recent h-index of 15 (since 2020), a distinguished researcher at Universität Ulm, specializes in the field of Machine Learning, Deep Learning, Affective Computing.

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

Enhanced Performance Prediction of ATL Model Transformations

NEMO–Eine App für das Nebenwirkungs-Management in der gastrointestinalen Onkologie (NEMO)

Predicting the Performance of ATL Model Transformations

Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images

Machine learning-based pain intensity estimation: Where pattern recognition meets chaos theory—an example based on the biovid heat pain database

Deep Learning Architectures for Pain Recognition Based on Physiological Signals

Multi-modal pain intensity assessment based on physiological signals: A deep learning perspective

Perspective on mHealth concepts to ensure users’ empowerment–from adverse event tracking for COVID-19 vaccinations to oncological treatment

Patrick Thiam Information

University

Position

Institute of Neural Information Processing Germany

Citations(all)

900

Citations(since 2020)

759

Cited By

443

hIndex(all)

16

hIndex(since 2020)

15

i10Index(all)

23

i10Index(since 2020)

20

Email

University Profile Page

Google Scholar

Patrick Thiam Skills & Research Interests

Machine Learning

Deep Learning

Affective Computing

Top articles of Patrick Thiam

Enhanced Performance Prediction of ATL Model Transformations

Performance Evaluation

2024/4/5

NEMO–Eine App für das Nebenwirkungs-Management in der gastrointestinalen Onkologie (NEMO)

Zeitschrift für Gastroenterologie

2023/8

Predicting the Performance of ATL Model Transformations

2023/4/15

Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images

Frontiers in Artificial Intelligence

2023/2/9

Patrick Thiam
Patrick Thiam

H-Index: 12

Ludwig Lausser
Ludwig Lausser

H-Index: 11

Machine learning-based pain intensity estimation: Where pattern recognition meets chaos theory—an example based on the biovid heat pain database

IEEE Access

2022/9/22

Deep Learning Architectures for Pain Recognition Based on Physiological Signals

2022/8/21

Patrick Thiam
Patrick Thiam

H-Index: 12

Friedhelm Schwenker
Friedhelm Schwenker

H-Index: 22

Multi-modal pain intensity assessment based on physiological signals: A deep learning perspective

Frontiers in Physiology

2021/9/1

Perspective on mHealth concepts to ensure users’ empowerment–from adverse event tracking for COVID-19 vaccinations to oncological treatment

IEEE Access

2021/6/7

Digitalization of adverse event management in oncology to improve treatment outcome—A prospective study protocol

Plos one

2021/6/4

Information fusion mechanisms for multi-modal affect recognition

2021/4/16

Patrick Thiam
Patrick Thiam

H-Index: 12

Using meta labels for the training of weighting models in a sample-specific late fusion classification architecture

2021/1/10

Dominant channel fusion architectures-an intelligent late fusion approach

2020/7/19

Two-stream attention network for pain recognition from video sequences

Sensors

2020/2/4

Patrick Thiam
Patrick Thiam

H-Index: 12

Friedhelm Schwenker
Friedhelm Schwenker

H-Index: 22

Multimodal Deep Denoising Convolutional Autoencoders for Pain Intensity Classification based on Physiological Signals.

2020/2

Patrick Thiam
Patrick Thiam

H-Index: 12

Friedhelm Schwenker
Friedhelm Schwenker

H-Index: 22

Subject-independent Pain Recognition using Physiological Signals and Para-linguistic Vocalizations.

2020/2

Patrick Thiam
Patrick Thiam

H-Index: 12

Friedhelm Schwenker
Friedhelm Schwenker

H-Index: 22

Automatic Pain Intensity Recognition: Training Set Selection based on Outliers and Centroids.

2020

Pain intensity recognition-an analysis of short-time sequences in a real-world scenario

2020

Person Identification based on Physiological Signals: Conditions and Risks.

2020

See List of Professors in Patrick Thiam University(Universität Ulm)

Co-Authors

academic-engine