Saved in:
| Main Authors: | Bülte, Christopher, Scholl, Philipp, Kutyniok, Gitta |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.12902 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Improved probabilistic regression using diffusion models
by: Kneissl, Carlo, et al.
Published: (2025)
by: Kneissl, Carlo, et al.
Published: (2025)
Graph Neural Networks for Enhancing Ensemble Forecasts of Extreme Rainfall
by: Bülte, Christopher, et al.
Published: (2025)
by: Bülte, Christopher, et al.
Published: (2025)
Uncertainty Quantification for Regression: A Unified Framework based on kernel scores
by: Bülte, Christopher, et al.
Published: (2025)
by: Bülte, Christopher, et al.
Published: (2025)
Robust identifiability for symbolic recovery of differential equations
by: Hauger, Hillary, et al.
Published: (2024)
by: Hauger, Hillary, et al.
Published: (2024)
ParFam -- (Neural Guided) Symbolic Regression Based on Continuous Global Optimization
by: Scholl, Philipp, et al.
Published: (2023)
by: Scholl, Philipp, et al.
Published: (2023)
Symbolic Recovery of Differential Equations: The Identifiability Problem
by: Scholl, Philipp, et al.
Published: (2022)
by: Scholl, Philipp, et al.
Published: (2022)
An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression
by: Bülte, Christopher, et al.
Published: (2025)
by: Bülte, Christopher, et al.
Published: (2025)
Random Spiking Neural Networks are Stable and Spectrally Simple
by: Araya, Ernesto, et al.
Published: (2025)
by: Araya, Ernesto, et al.
Published: (2025)
Generalization Bounds for Message Passing Networks on Mixture of Graphons
by: Maskey, Sohir, et al.
Published: (2024)
by: Maskey, Sohir, et al.
Published: (2024)
Interpretable Robotic Friction Learning via Symbolic Regression
by: Scholl, Philipp, et al.
Published: (2025)
by: Scholl, Philipp, et al.
Published: (2025)
When is a System Discoverable from Data? Discovery Requires Chaos
by: Shumaylov, Zakhar, et al.
Published: (2025)
by: Shumaylov, Zakhar, et al.
Published: (2025)
Mathematical Algorithm Design for Deep Learning under Societal and Judicial Constraints: The Algorithmic Transparency Requirement
by: Boche, Holger, et al.
Published: (2024)
by: Boche, Holger, et al.
Published: (2024)
Uncertainty quantification for data-driven weather models
by: Bülte, Christopher, et al.
Published: (2024)
by: Bülte, Christopher, et al.
Published: (2024)
Learning-based adaption of robotic friction models
by: Scholl, Philipp, et al.
Published: (2023)
by: Scholl, Philipp, et al.
Published: (2023)
Weisfeiler and Leman Go Loopy: A New Hierarchy for Graph Representational Learning
by: Paolino, Raffaele, et al.
Published: (2024)
by: Paolino, Raffaele, et al.
Published: (2024)
Dataset of Pathloss and ToA Radio Maps With Localization Application
by: Yapar, Çağkan, et al.
Published: (2022)
by: Yapar, Çağkan, et al.
Published: (2022)
Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time
by: Nguyen, Duc Anh, et al.
Published: (2025)
by: Nguyen, Duc Anh, et al.
Published: (2025)
Probabilistic operator learning: generative modeling and uncertainty quantification for foundation models of differential equations
by: Zhang, Benjamin J., et al.
Published: (2025)
by: Zhang, Benjamin J., et al.
Published: (2025)
Revisiting Glorot Initialization for Long-Range Linear Recurrences
by: Bar, Noga, et al.
Published: (2025)
by: Bar, Noga, et al.
Published: (2025)
Conflicting Biases at the Edge of Stability: Norm versus Sharpness Regularization
by: Matveev, Maria, et al.
Published: (2025)
by: Matveev, Maria, et al.
Published: (2025)
Graph Representational Learning: When Does More Expressivity Hurt Generalization?
by: Maskey, Sohir, et al.
Published: (2025)
by: Maskey, Sohir, et al.
Published: (2025)
Adaptive-CaRe: Adaptive Causal Regularization for Robust Outcome Prediction
by: Bhasker, Nithya, et al.
Published: (2026)
by: Bhasker, Nithya, et al.
Published: (2026)
Probabilistic computation and uncertainty quantification with emerging covariance
by: Ma, Hengyuan, et al.
Published: (2023)
by: Ma, Hengyuan, et al.
Published: (2023)
GradPCA: Leveraging NTK Alignment for Reliable Out-of-Distribution Detection
by: Seleznova, Mariia, et al.
Published: (2025)
by: Seleznova, Mariia, et al.
Published: (2025)
Computability of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility through Quantization
by: Boche, Holger, et al.
Published: (2024)
by: Boche, Holger, et al.
Published: (2024)
Sparse-Aware Neural Networks for Nonlinear Functionals: Mitigating the Exponential Dependence on Dimension
by: Li, Jianfei, et al.
Published: (2026)
by: Li, Jianfei, et al.
Published: (2026)
Distribution free uncertainty quantification in neuroscience-inspired deep operators
by: Garg, Shailesh, et al.
Published: (2024)
by: Garg, Shailesh, et al.
Published: (2024)
Deep set based operator learning with uncertainty quantification
by: Ma, Lei, et al.
Published: (2025)
by: Ma, Lei, et al.
Published: (2025)
Improved uncertainty quantification for neural networks with Bayesian last layer
by: Fiedler, Felix, et al.
Published: (2023)
by: Fiedler, Felix, et al.
Published: (2023)
Modeling Spatial Extremal Dependence of Precipitation Using Distributional Neural Networks
by: Bülte, Christopher, et al.
Published: (2024)
by: Bülte, Christopher, et al.
Published: (2024)
The Price of Robustness: Stable Classifiers Need Overparameterization
by: von Berg, Jonas, et al.
Published: (2026)
by: von Berg, Jonas, et al.
Published: (2026)
Understanding Multimodal Failure in Action-Chunking Behavioral Cloning
by: Mazza, Lorenzo, et al.
Published: (2026)
by: Mazza, Lorenzo, et al.
Published: (2026)
Fast and reliable uncertainty quantification with neural network ensembles for industrial image classification
by: Thuy, Arthur, et al.
Published: (2024)
by: Thuy, Arthur, et al.
Published: (2024)
Active operator learning with predictive uncertainty quantification for partial differential equations
by: Winovich, Nick, et al.
Published: (2025)
by: Winovich, Nick, et al.
Published: (2025)
Structure and asymptotic preserving deep neural surrogates for uncertainty quantification in multiscale kinetic equations
by: Chen, Wei, et al.
Published: (2025)
by: Chen, Wei, et al.
Published: (2025)
DADEE: Well-calibrated uncertainty quantification in neural networks for barriers-based robot safety
by: Ataei, Masoud, et al.
Published: (2024)
by: Ataei, Masoud, et al.
Published: (2024)
The surrogate Gibbs-posterior of a corrected stochastic MALA: Towards uncertainty quantification for neural networks
by: Bieringer, Sebastian, et al.
Published: (2023)
by: Bieringer, Sebastian, et al.
Published: (2023)
Safe learning-based control via function-based uncertainty quantification
by: Tokmak, Abdullah, et al.
Published: (2026)
by: Tokmak, Abdullah, et al.
Published: (2026)
Quantum parameter estimation with uncertainty quantification from continuous measurement data using neural network ensembles
by: Anteneh, Amanuel
Published: (2025)
by: Anteneh, Amanuel
Published: (2025)
Incorporating uncertainty quantification into travel mode choice modeling: a Bayesian neural network (BNN) approach and an uncertainty-guided active survey framework
by: Zheng, Shuwen, et al.
Published: (2024)
by: Zheng, Shuwen, et al.
Published: (2024)
Similar Items
-
Improved probabilistic regression using diffusion models
by: Kneissl, Carlo, et al.
Published: (2025) -
Graph Neural Networks for Enhancing Ensemble Forecasts of Extreme Rainfall
by: Bülte, Christopher, et al.
Published: (2025) -
Uncertainty Quantification for Regression: A Unified Framework based on kernel scores
by: Bülte, Christopher, et al.
Published: (2025) -
Robust identifiability for symbolic recovery of differential equations
by: Hauger, Hillary, et al.
Published: (2024) -
ParFam -- (Neural Guided) Symbolic Regression Based on Continuous Global Optimization
by: Scholl, Philipp, et al.
Published: (2023)