1 citations · 2 across the 6 of their papers we have counts for
6 papers
Amortized Probabilistic Retrieval of Atmospheric CO2 from OCO-2 Spectra Using Deep Learning with Laplace Approximations and Normalizing Flows
Alejandro Calle-Saldarriaga, Felix Jimenez, Jack Grosskreuz +3
Space-based monitoring of atmospheric carbon dioxide (CO) constrains the global carbon budget. NASA's Orbiting Carbon Observatory-2 (OCO-2) estimates column-averaged dry-air mo…
Restore-R1: Efficient Image Restoration Agents via Reinforcement Learning with Multimodal LLM Perceptual Feedback
Jianglin Lu, Yuanwei Wu, Ziyi Zhao +4
Complex image restoration aims to recover high-quality images from inputs affected by multiple degradations such as blur, noise, rain, and compression artifacts. Recent restoration…
Probabilistic Skip Connections for Deterministic Uncertainty Quantification in Deep Neural Networks
Felix Jimenez, Matthias Katzfuss
Deterministic uncertainty quantification (UQ) in deep learning aims to estimate uncertainty with a single pass through a network by leveraging outputs from the network's feature ex…
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks
Felix Jimenez, Matthias Katzfuss
For regression tasks, standard Gaussian processes (GPs) provide natural uncertainty quantification (UQ), while deep neural networks (DNNs) excel at representation learning. Determi…
Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization
Jian Cao, Myeongjong Kang, Felix Jimenez +3
To achieve scalable and accurate inference for latent Gaussian processes, we propose a variational approximation based on a family of Gaussian distributions whose covariance matric…
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes
Felix Jimenez, Matthias Katzfuss
Bayesian optimization is a technique for optimizing black-box target functions. At the core of Bayesian optimization is a surrogate model that predicts the output of the target fun…