15 citations · 39 across the 10 of their papers we have counts for
12 papers
Quantifying the Uncertainty of Foundation Models with Singular Value Ensembles
Mehmet Ozgur Turkoglu, Dominik J. Mühlematter, Alexander Becker +2
Foundation models have become a dominant paradigm in machine learning, achieving remarkable performance across diverse tasks through large-scale pretraining. However, they often yi…
Continuous Space-Time Video Super-Resolution with 3D Fourier Fields
Alexander Becker, Julius Erbach, Dominik Narnhofer +1
We introduce a novel formulation for continuous space-time video super-resolution. Instead of decoupling the representation of a video sequence into separate spatial and temporal c…
Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion
Massimiliano Viola, Kevin Qu, Nando Metzger +4
Depth completion upgrades sparse depth measurements into dense depth maps guided by a conventional image. Existing methods for this highly ill-posed task operate in tightly constra…
The unrealized potential of agroforestry for an emissions-intensive agricultural commodity
Alexander Becker, Jan D. Wegner, Evans Dawoe +7
Reconciling agricultural production with climate-change mitigation is a formidable sustainability problem. Retaining trees in agricultural systems is one proposed solution, but the…
LoRA-Ensemble: Efficient Uncertainty Modelling for Self-Attention Networks
Dominik J. Mühlematter, Michelle Halbheer, Alexander Becker +4
Numerous real-world decisions rely on machine learning algorithms and require calibrated uncertainty estimates. However, modern methods often yield overconfident, uncalibrated pred…
Thera: Aliasing-Free Arbitrary-Scale Super-Resolution with Neural Heat Fields
Alexander Becker, Rodrigo Caye Daudt, Dominik Narnhofer +4
Recent approaches to arbitrary-scale single image super-resolution (ASR) use neural fields to represent continuous signals that can be sampled at arbitrary resolutions. However, po…
Certified Data Removal in Sum-Product Networks
Alexander Becker, Thomas Liebig
Data protection regulations like the GDPR or the California Consumer Privacy Act give users more control over the data that is collected about them. Deleting the collected data is…
Evaluating Machine Unlearning via Epistemic Uncertainty
Alexander Becker, Thomas Liebig
There has been a growing interest in Machine Unlearning recently, primarily due to legal requirements such as the General Data Protection Regulation (GDPR) and the California Consu…
FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation
Mehmet Ozgur Turkoglu, Alexander Becker, Hüseyin Anil Gündüz +6
The ability to estimate epistemic uncertainty is often crucial when deploying machine learning in the real world, but modern methods often produce overconfident, uncalibrated uncer…
Learning Graph Regularisation for Guided Super-Resolution
Riccardo de Lutio, Alexander Becker, Stefano D'Aronco +3
We introduce a novel formulation for guided super-resolution. Its core is a differentiable optimisation layer that operates on a learned affinity graph. The learned graph potential…
Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Deep Ensembles
Alexander Becker, Stefania Russo, Stefano Puliti +3
Monitoring and managing Earth's forests in an informed manner is an important requirement for addressing challenges like biodiversity loss and climate change. While traditional in…
Deep Learning for Virtual Screening: Five Reasons to Use ROC Cost Functions
Vladimir Golkov, Alexander Becker, Daniel T. Plop +6
Computer-aided drug discovery is an essential component of modern drug development. Therein, deep learning has become an important tool for rapid screening of billions of molecules…