31 citations · 31 across the 4 of their papers we have counts for
4 papers
Supervised Distributional Reduction via Optimal Transport and Dependence Maximization
Sai-Aakash Ramesh, Archit Sood, Andrew Corbett +1
Learning representations that capture both intrinsic data geometry and target-relevant structure remains a fundamental challenge, particularly in settings where data reduction must…
Boosting Inference with Guided Reasoning: Stochastic Exploration for Recursive Models
Andrew Corbett, Archit Sood, Anna Tzatzopoulou +2
Recent work on recursive architectures has shown that tiny neural networks can be surprisingly powerful on structured reasoning tasks. The trick is to model reasoning trajectories…
Democratizing Uncertainty Quantification
Linus Seelinger, Anne Reinarz, Mikkel B. Lykkegaard +22
Uncertainty Quantification (UQ) is vital to safety-critical model-based analyses, but the widespread adoption of sophisticated UQ methods is limited by technical complexity. In thi…
Accelerating Uncertainty Quantification of Groundwater Flow Modelling Using a Deep Neural Network Proxy
Mikkel B. Lykkegaard, Tim J. Dodwell, David Moxey
Quantifying the uncertainty in model parameters and output is a critical component in model-driven decision support systems for groundwater management. This paper presents a novel…