:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Aretos, E. V., Sotiropoulos, D. G.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2409.05206
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Conditional Shift-Robust Conformal Prediction for Graph Neural Network
by: Akansha, S.
Published: (2024)

Introducing Interval Neural Networks for Uncertainty-Aware System Identification
by: Ferah, Mehmet Ali, et al.
Published: (2025)

Explainable fault and severity classification for rolling element bearings using Kolmogorov-Arnold networks
by: Rigas, Spyros, et al.
Published: (2024)

Z-Error Loss for Training Neural Networks
by: Godin, Guillaume
Published: (2025)

Enhancing Interval Type-2 Fuzzy Logic Systems: Learning for Precision and Prediction Intervals
by: Koklu, Ata, et al.
Published: (2024)

CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks
by: Wang, Kaizheng, et al.
Published: (2024)

Activation by Interval-wise Dropout: A Simple Way to Prevent Neural Networks from Plasticity Loss
by: Park, Sangyeon, et al.
Published: (2025)

Tube Loss: A Novel Approach for Prediction Interval Estimation
by: Anand, Pritam, et al.
Published: (2024)

Feature Shift Localization Network
by: Barrabés, Míriam, et al.
Published: (2025)

The Cost of Relaxation: Evaluating the Error in Convex Neural Network Verification
by: Papamichail, Merkouris, et al.
Published: (2026)

Neural Horizon Model Predictive Control -- Increasing Computational Efficiency with Neural Networks
by: Alsmeier, Hendrik, et al.
Published: (2024)

A Comparison of Methods for Neural Network Aggregation
by: Pomerat, John, et al.
Published: (2023)

CONTINA: Confidence Interval for Traffic Demand Prediction with Coverage Guarantee
by: Yang, Chao, et al.
Published: (2025)

Unveil Sources of Uncertainty: Feature Contribution to Conformal Prediction Intervals
by: Idrissi, Marouane Il, et al.
Published: (2025)

CLUE: Neural Networks Calibration via Learning Uncertainty-Error alignment
by: Mendes, Pedro, et al.
Published: (2025)

Learning for Interval Prediction of Electricity Demand: A Cluster-based Bootstrapping Approach
by: Dube, Rohit, et al.
Published: (2023)

Mask-PINNs: Mitigating Internal Covariate Shift in Physics-Informed Neural Networks
by: Jiang, Feilong, et al.
Published: (2025)

Aligning Evaluation with Clinical Priorities: Calibration, Label Shift, and Error Costs
by: Flores, Gerardo A., et al.
Published: (2025)

On Newton's Method to Unlearn Neural Networks
by: Bui, Nhung, et al.
Published: (2024)

Learning from Yesterday's Error: An Efficient Online Learning Method for Traffic Demand Prediction
by: Huang, Xiannan, et al.
Published: (2026)

ShiftAddAug: Augment Multiplication-Free Tiny Neural Network with Hybrid Computation
by: Guo, Yipin, et al.
Published: (2024)

Towards Trustworthy Vital Sign Forecasting: Leveraging Uncertainty for Prediction Intervals
by: Wang, Li Rong, et al.
Published: (2025)

QuantSightBench: Evaluating LLM Quantitative Forecasting with Prediction Intervals
by: Qin, Jeremy, et al.
Published: (2026)

A Functional Perspective on Knowledge Distillation in Neural Networks
by: Mason-Williams, Israel, et al.
Published: (2025)

ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks
by: You, Haoran, et al.
Published: (2022)

Scalable Simulation-Based Model Inference with Test-Time Complexity Control
by: Gloeckler, Manuel, et al.
Published: (2026)

ShapShift: Explaining Model Prediction Shifts with Subgroup Conditional Shapley Values
by: Bewley, Tom, et al.
Published: (2026)

Learning When to Act: Interval-Aware Reinforcement Learning with Predictive Temporal Structure
by: Di Gioia, Davide
Published: (2026)

Quantifying Calibration Error in Neural Networks Through Evidence-Based Theory
by: Ouattara, Koffi Ismael, et al.
Published: (2024)

Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices Approach
by: Li, Guoming, et al.
Published: (2024)

Conformalized Link Prediction on Graph Neural Networks
by: Zhao, Tianyi, et al.
Published: (2024)

Short-term Streamflow and Flood Forecasting based on Graph Convolutional Recurrent Neural Network and Residual Error Learning
by: Pan, Xiyu, et al.
Published: (2024)

Out-of-distribution Reject Option Method for Dataset Shift Problem in Early Disease Onset Prediction
by: Tosaki, Taisei, et al.
Published: (2024)

Conformal Prediction Adaptive to Unknown Subpopulation Shifts
by: Wang, Nien-Shao, et al.
Published: (2025)

Developing the Temporal Graph Convolutional Neural Network Model to Predict Hip Replacement using Electronic Health Records
by: Hancox, Zoe, et al.
Published: (2024)

Trustworthy Graph Neural Networks: Aspects, Methods and Trends
by: Zhang, He, et al.
Published: (2022)

IDInit: A Universal and Stable Initialization Method for Neural Network Training
by: Pan, Yu, et al.
Published: (2025)

GRExplainer: A Universal Explanation Method for Temporal Graph Neural Networks
by: Li, Xuyan, et al.
Published: (2025)

Zadeh's Type-2 Fuzzy Logic Systems: Precision and High-Quality Prediction Intervals
by: Guven, Yusuf, et al.
Published: (2024)

Is More Context Always Better? Examining LLM Reasoning Capability for Time Interval Prediction
by: Cao, Yanan, et al.
Published: (2026)