6 citations · 6 across the 7 of their papers we have counts for
10 papers
The zero divisor and augmentation conjectures for medial quandle rings
Luc Ta
For quandle rings, Bardakov--Passi--Singh and Elhamdadi--Nunez--Singh respectively formulated an analogue of Kaplansky's zero divisor conjecture and a weakened analogue of Kaplansk…
SMEFT Constraints via Quantum Entanglement in Portal-Mediated Scattering
Shilpa Jangid, Hiroshi Okada
We study Standard Model Effective Field Theory (SMEFT) extensions in portal-mediated scattering processes from the perspective of quantum entanglement. Quantum information metrics…
Uncertainty-Aware End-to-End AI Weather Forecasting: Disentangling Observation and Model Contributions
Rodrigo Almeida, Noelia Otero, Jost Arndt +3
End-to-end weather forecasting systems produce skillful global gridded and station forecasts directly from raw Earth observations, replacing the numerical weather prediction pipeli…
Beyond Boundary Noise: Aggregated Aleatoric Uncertainty Fails to Capture Presence Ambiguity in 3D Lung Nodule Segmentation
Simon Baur, Arne Schernich, Ekin Böke +2
Uncertainty estimation is critical for the safe clinical deployment of deep learning in medical image segmentation, with aleatoric uncertainty theoretically designed to capture irr…
Multimodal Deep Learning for Prediction of Progression-Free Survival in Patients with Neuroendocrine Tumors Undergoing 177Lu-based Peptide Receptor Radionuclide Therapy
Simon Baur, Tristan Ruhwedel, Ekin Böke +11
Peptide receptor radionuclide therapy (PRRT) is an established treatment for metastatic neuroendocrine tumors (NETs), yet long-term disease control occurs only in a subset of patie…
Fractional Diffusion Bridge Models
Gabriel Nobis, Maximilian Springenberg, Arina Belova +5
We present Fractional Diffusion Bridge Models (FDBM), a novel generative diffusion bridge framework driven by an approximation of the rich and non-Markovian fractional Brownian mot…
Benchmarking Uncertainty and its Disentanglement in multi-label Chest X-Ray Classification
Simon Baur, Wojciech Samek, Jackie Ma
Reliable uncertainty quantification is crucial for trustworthy decision-making and the deployment of AI models in medical imaging. While prior work has explored the ability of neur…
Synthetic Datasets for Machine Learning on Spatio-Temporal Graphs using PDEs
Jost Arndt, Utku Isil, Michael Detzel +2
Many physical processes can be expressed through partial differential equations (PDEs). Real-world measurements of such processes are often collected at irregularly distributed poi…
Spatial Shortcuts in Graph Neural Controlled Differential Equations
Michael Detzel, Gabriel Nobis, Jackie Ma +1
We incorporate prior graph topology information into a Neural Controlled Differential Equation (NCDE) to predict the future states of a dynamical system defined on a graph. The inf…
DMLR: Data-centric Machine Learning Research -- Past, Present and Future
Luis Oala, Manil Maskey, Lilith Bat-Leah +35
Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure developm…