Saved in:
| Main Authors: | Grefsrud, Aurora, Blaser, Nello, Buanes, Trygve |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.11460 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
SO(3)-Equivariant Neural Networks for Learning from Scalar and Vector Fields on Spheres
by: Ballerin, Francesco, et al.
Published: (2025)
by: Ballerin, Francesco, et al.
Published: (2025)
RotaTouille: Rotation Equivariant Deep Learning for Contours
by: Gardaa, Odin Hoff, et al.
Published: (2025)
by: Gardaa, Odin Hoff, et al.
Published: (2025)
Evaluating Prediction Uncertainty Estimates from BatchEnsemble
by: Blørstad, Morten, et al.
Published: (2026)
by: Blørstad, Morten, et al.
Published: (2026)
Stable Update of Regression Trees
by: Blørstad, Morten, et al.
Published: (2024)
by: Blørstad, Morten, et al.
Published: (2024)
No Triangulation Without Representation: Generalization in Topological Deep Learning
by: Schmidt, Johannes S., et al.
Published: (2026)
by: Schmidt, Johannes S., et al.
Published: (2026)
Machine Learning Classification of Sphalerons and Black Holes at the LHC
by: Grefsrud, Aurora Singstad, et al.
Published: (2023)
by: Grefsrud, Aurora Singstad, et al.
Published: (2023)
Explainable classification of astronomical uncertain time series
by: Mbouopda, Michael Franklin, et al.
Published: (2022)
by: Mbouopda, Michael Franklin, et al.
Published: (2022)
Trust the uncertain teacher: distilling dark knowledge via calibrated uncertainty
by: Kim, Jeonghyun, et al.
Published: (2026)
by: Kim, Jeonghyun, et al.
Published: (2026)
Tempo estimation as fully self-supervised binary classification
by: Henkel, Florian, et al.
Published: (2024)
by: Henkel, Florian, et al.
Published: (2024)
A metrological framework for uncertainty evaluation in machine learning classification models
by: Bilson, Samuel, et al.
Published: (2025)
by: Bilson, Samuel, et al.
Published: (2025)
Hoeffding adaptive trees for multi-label classification on data streams
by: Esteban, Aurora, et al.
Published: (2024)
by: Esteban, Aurora, et al.
Published: (2024)
Awareness of uncertainty in classification using a multivariate model and multi-views
by: Kornaev, Alexey, et al.
Published: (2024)
by: Kornaev, Alexey, et al.
Published: (2024)
A method for classification of data with uncertainty using hypothesis testing
by: Yokura, Shoma, et al.
Published: (2025)
by: Yokura, Shoma, et al.
Published: (2025)
Omics-driven hybrid dynamic modeling of bioprocesses with uncertainty estimation
by: Espinel-Ríos, Sebastián, et al.
Published: (2024)
by: Espinel-Ríos, Sebastián, et al.
Published: (2024)
Adaptive kernel-density approach for imbalanced binary classification
by: Nishimura, Kotaro J., et al.
Published: (2025)
by: Nishimura, Kotaro J., et al.
Published: (2025)
An uncertainty-aware Bayesian framework for machine learning classification models: A case study in land cover classification
by: Bilson, Samuel, et al.
Published: (2025)
by: Bilson, Samuel, et al.
Published: (2025)
Robust support vector model based on bounded asymmetric elastic net loss for binary classification
by: Du, Haiyan, et al.
Published: (2026)
by: Du, Haiyan, et al.
Published: (2026)
BLIA: Detect model memorization in binary classification model through passive Label Inference attack
by: Khan, Mohammad Wahiduzzaman, et al.
Published: (2025)
by: Khan, Mohammad Wahiduzzaman, et al.
Published: (2025)
Legitimate ground-truth-free metrics for deep uncertainty classification scoring
by: Pignet, Arthur, et al.
Published: (2024)
by: Pignet, Arthur, et al.
Published: (2024)
Asymptotic and Finite Sample Analysis of Nonexpansive Stochastic Approximations with Markovian Noise
by: Blaser, Ethan, et al.
Published: (2024)
by: Blaser, Ethan, et al.
Published: (2024)
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)
Position: Epistemic uncertainty estimation methods are fundamentally incomplete
by: Jiménez, Sebastián, et al.
Published: (2025)
by: Jiménez, Sebastián, et al.
Published: (2025)
Safe reinforcement learning in uncertain contexts
by: Baumann, Dominik, et al.
Published: (2024)
by: Baumann, Dominik, et al.
Published: (2024)
Do you understand epistemic uncertainty? Think again! Rigorous frequentist epistemic uncertainty estimation in regression
by: Foglia, Enrico, et al.
Published: (2025)
by: Foglia, Enrico, et al.
Published: (2025)
Medical artificial intelligence toolbox (MAIT): an explainable machine learning framework for binary classification, survival modelling, and regression analyses
by: Marandi, Ramtin Zargari, et al.
Published: (2025)
by: Marandi, Ramtin Zargari, et al.
Published: (2025)
Sharp concentration of uniform generalization errors in binary linear classification
by: Nakakita, Shogo
Published: (2025)
by: Nakakita, Shogo
Published: (2025)
Synergistic eigenanalysis of covariance and Hessian matrices for enhanced binary classification
by: Hartoyo, Agus, et al.
Published: (2024)
by: Hartoyo, Agus, et al.
Published: (2024)
Federated Linear Contextual Bandits with Heterogeneous Clients
by: Blaser, Ethan, et al.
Published: (2024)
by: Blaser, Ethan, et al.
Published: (2024)
Almost Sure Convergence of Differential Temporal Difference Learning for Average Reward Markov Decision Processes
by: Blaser, Ethan, et al.
Published: (2026)
by: Blaser, Ethan, et al.
Published: (2026)
ProteoKnight: Convolution-based phage virion protein classification and uncertainty analysis
by: Neha, Samiha Afaf, et al.
Published: (2025)
by: Neha, Samiha Afaf, et al.
Published: (2025)
Time-dependent density estimation using binary classifiers
by: Dasgupta, Agnimitra, et al.
Published: (2025)
by: Dasgupta, Agnimitra, et al.
Published: (2025)
Transformers Can Learn Temporal Difference Methods for In-Context Reinforcement Learning
by: Wang, Jiuqi, et al.
Published: (2024)
by: Wang, Jiuqi, et al.
Published: (2024)
PDE-constrained Gaussian process surrogate modeling with uncertain data locations
by: Ye, Dongwei, et al.
Published: (2023)
by: Ye, Dongwei, et al.
Published: (2023)
An experimental comparative study of backpropagation and alternatives for training binary neural networks for image classification
by: Crulis, Ben, et al.
Published: (2024)
by: Crulis, Ben, et al.
Published: (2024)
A review of predictive uncertainty estimation with machine learning
by: Tyralis, Hristos, et al.
Published: (2022)
by: Tyralis, Hristos, et al.
Published: (2022)
Retro-fallback: retrosynthetic planning in an uncertain world
by: Tripp, Austin, et al.
Published: (2023)
by: Tripp, Austin, et al.
Published: (2023)
Data organization limits the predictability of binary classification
by: Jing, Fei, et al.
Published: (2024)
by: Jing, Fei, et al.
Published: (2024)
Evaluating deep learning models for fault diagnosis of a rotating machinery with epistemic and aleatoric uncertainty
by: Jalayer, Reza, et al.
Published: (2024)
by: Jalayer, Reza, et al.
Published: (2024)
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials
by: Holzenkamp, Matthias, et al.
Published: (2024)
by: Holzenkamp, Matthias, et al.
Published: (2024)
Asymptotic Bayes risk of semi-supervised learning with uncertain labeling
by: Leger, Victor, et al.
Published: (2024)
by: Leger, Victor, et al.
Published: (2024)
Similar Items
-
SO(3)-Equivariant Neural Networks for Learning from Scalar and Vector Fields on Spheres
by: Ballerin, Francesco, et al.
Published: (2025) -
RotaTouille: Rotation Equivariant Deep Learning for Contours
by: Gardaa, Odin Hoff, et al.
Published: (2025) -
Evaluating Prediction Uncertainty Estimates from BatchEnsemble
by: Blørstad, Morten, et al.
Published: (2026) -
Stable Update of Regression Trees
by: Blørstad, Morten, et al.
Published: (2024) -
No Triangulation Without Representation: Generalization in Topological Deep Learning
by: Schmidt, Johannes S., et al.
Published: (2026)