1 citations · 1 across the 11 of their papers we have counts for
12 papers
Collaborative Synthetic Data for Privacy-Preserving Financial Fraud Detection Across Organizational Silos
Simeon Allmendinger, Domenique Zipperling, Burhanettin Bahadir Kibar +2
Organizations seek analytical value from AI, yet relevant data are often fragmented across organizations and constrained by privacy. This is acute in financial fraud detection, whe…
Where are the Humans? A Scoping Review of Fairness in Multi-agent AI Systems
Simeon Allmendinger, Luca Deck, Lucas Mueller
Rapid advances in Generative AI are giving rise to increasingly sophisticated Multi-Agent AI (MAAI) systems. While AI fairness has been extensively studied in traditional predictiv…
Normative Common Ground Replication (NormCoRe): Replication-by-Translation for Studying Norms in Multi-Agent AI
Luca Deck, Simeon Allmendinger, Lucas Müller +1
In the late 2010s, the fashion trend NormCore framed sameness as a signal of belonging, illustrating how norms emerge through collective coordination. Today, similar forms of norma…
Reading Between the Tokens: Improving Preference Predictions through Mechanistic Forecasting
Sarah Ball, Simeon Allmendinger, Frauke Kreuter +1
Large language models are increasingly used to predict human preferences in both scientific and business endeavors, yet current approaches rely exclusively on analyzing model outpu…
Do Edges Matter? Investigating Edge-Enhanced Pre-Training for Medical Image Segmentation
Paul Zaha, Lars Böcking, Simeon Allmendinger +2
Medical image segmentation is crucial for disease diagnosis and treatment planning, yet developing robust segmentation models often requires substantial computational resources and…
Human Preferences in Large Language Model Latent Space: A Technical Analysis on the Reliability of Synthetic Data in Voting Outcome Prediction
Sarah Ball, Simeon Allmendinger, Frauke Kreuter +1
Generative AI (GenAI) is increasingly used in survey contexts to simulate human preferences. While many research endeavors evaluate the quality of synthetic GenAI data by comparing…
CollaFuse: Collaborative Diffusion Models
Simeon Allmendinger, Domenique Zipperling, Lukas Struppek +1
In the landscape of generative artificial intelligence, diffusion-based models have emerged as a promising method for generating synthetic images. However, the application of diffu…
Interactive Generation of Laparoscopic Videos with Diffusion Models
Ivan Iliash, Simeon Allmendinger, Felix Meissen +2
Generative AI, in general, and synthetic visual data generation, in specific, hold much promise for benefiting surgical training by providing photorealism to simulation environment…
CollaFuse: Navigating Limited Resources and Privacy in Collaborative Generative AI
Domenique Zipperling, Simeon Allmendinger, Lukas Struppek +1
In the landscape of generative artificial intelligence, diffusion-based models present challenges for socio-technical systems in data requirements and privacy. Traditional approach…
Navigating the Synthetic Realm: Harnessing Diffusion-based Models for Laparoscopic Text-to-Image Generation
Simeon Allmendinger, Patrick Hemmer, Moritz Queisner +5
Recent advances in synthetic imaging open up opportunities for obtaining additional data in the field of surgical imaging. This data can provide reliable supplements supporting sur…
Redefining the Laparoscopic Spatial Sense: AI-based Intra- and Postoperative Measurement from Stereoimages
Leopold Müller, Patrick Hemmer, Moritz Queisner +5
A significant challenge in image-guided surgery is the accurate measurement task of relevant structures such as vessel segments, resection margins, or bowel lengths. While this tas…
SurgT challenge: Benchmark of Soft-Tissue Trackers for Robotic Surgery
Joao Cartucho, Alistair Weld, Samyakh Tukra +23
This paper introduces the ``SurgT: Surgical Tracking" challenge which was organised in conjunction with MICCAI 2022. There were two purposes for the creation of this challenge: (1)…