Image de couverture de livre

Efficiently Transforming Neural Networks into Decision Trees: A Path to Ground Truth Explanations with RENTT

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Monke, Helena, Fresz, Benjamin, Bernreuther, Marco, Chen, Yilin, Huber, Marco F.
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

From Confusion to Clarity: ProtoScore -- A Framework for Evaluating Prototype-Based XAI
par: Monke, Helena, et autres
Publié: (2025)

Classification Metrics for Image Explanations: Towards Building Reliable XAI-Evaluations
par: Fresz, Benjamin, et autres
Publié: (2024)

Data Efficient Prediction of excited-state properties using Quantum Neural Networks
par: Hagelüken, Manuel, et autres
Publié: (2024)

From Overfitting to Reliability: Introducing the Hierarchical Approximate Bayesian Neural Network
par: Amirkhanian, Hayk, et autres
Publié: (2025)

Learning Quantifiable Visual Explanations Without Ground-Truth
par: Singh, Amritpal, et autres
Publié: (2026)

Evaluating Model Explanations without Ground Truth
par: Rawal, Kaivalya, et autres
Publié: (2025)

How should AI decisions be explained? Requirements for Explanations from the Perspective of European Law
par: Fresz, Benjamin, et autres
Publié: (2024)

Neural Networks Decoded: Targeted and Robust Analysis of Neural Network Decisions via Causal Explanations and Reasoning
par: Diallo, Alec F., et autres
Publié: (2024)

How Explanations Leak the Decision Logic: Stealing Graph Neural Networks via Explanation Alignment
par: Ma, Bin, et autres
Publié: (2025)

Sample-Efficient Bayesian Transfer Learning for Online Machine Parameter Optimization
par: Wagner, Philipp, et autres
Publié: (2025)

Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate Models
par: Engel, Andrew, et autres
Publié: (2023)

Truthfulness of Decision-Theoretic Calibration Measures
par: Qiao, Mingda, et autres
Publié: (2025)

Closing the Gap Between Synthetic and Ground Truth Time Series Distributions via Neural Mapping
par: Lee, Daesoo, et autres
Publié: (2025)

View-based Explanations for Graph Neural Networks
par: Chen, Tingyang, et autres
Publié: (2024)

Causal Mechanism Estimation in Multi-Sensor Systems Across Multiple Domains
par: Yu, Jingyi, et autres
Publié: (2025)

GNN-DT: Graph Neural Network Enhanced Decision Transformer for Efficient Optimization in Dynamic Environments
par: Orfanoudakis, Stavros, et autres
Publié: (2025)

HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories
par: Hedlin, Eric, et autres
Publié: (2024)

A Novel Explanation Against Linear Neural Networks
par: Lakkapragada, Anish
Publié: (2023)

Conformalized Credal Regions for Classification with Ambiguous Ground Truth
par: Caprio, Michele, et autres
Publié: (2024)

SEEN: Sharpening Explanations for Graph Neural Networks using Explanations from Neighborhoods
par: Cho, Hyeoncheol, et autres
Publié: (2021)

Calibration without Ground Truth
par: Kong, Yuqing, et autres
Publié: (2026)

Improving the Validity of Decision Trees as Explanations
par: Nemecek, Jiri, et autres
Publié: (2023)

Reachability In Simple Neural Networks
par: Sälzer, Marco, et autres
Publié: (2022)

A Gap Between Decision Trees and Neural Networks
par: Kumar, Akash
Publié: (2026)

Confidence Calibration under Ambiguous Ground Truth
par: Tao, Linwei, et autres
Publié: (2026)

auto-sktime: Automated Time Series Forecasting
par: Zöller, Marc-André, et autres
Publié: (2023)

Hybrid Dual-Path Linear Transformations for Efficient Transformer Architectures
par: Khasia, Vladimer
Publié: (2026)

SAGE: Scalable Ground Truth Evaluations for Large Sparse Autoencoders
par: Venhoff, Constantin, et autres
Publié: (2024)

Faster Verified Explanations for Neural Networks
par: De Palma, Alessandro, et autres
Publié: (2025)

Graph Neural Network Explanations are Fragile
par: Li, Jiate, et autres
Publié: (2024)

Output-Constrained Decision Trees
par: Tunç, Hüseyin, et autres
Publié: (2024)

Reinforcement learning-based architecture search for quantum machine learning
par: Rapp, Frederic, et autres
Publié: (2024)

Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks
par: Zhang, Jiaxing, et autres
Publié: (2025)

Multi-Constitutive Neural Network for Large Deformation Poromechanics Problem
par: Zhang, Qi, et autres
Publié: (2020)

Synthetic Data and the Shifting Ground of Truth
par: Offenhuber, Dietmar
Publié: (2025)

Temporal Decisions: Leveraging Temporal Correlation for Efficient Decisions in Early Exit Neural Networks
par: Sponner, Max, et autres
Publié: (2024)

FovEx: Human-Inspired Explanations for Vision Transformers and Convolutional Neural Networks
par: Panda, Mahadev Prasad, et autres
Publié: (2024)

Evaluation of Missing Data Imputation for Time Series Without Ground Truth
par: Farjallah, Rania, et autres
Publié: (2025)

Network Tomography with Path-Centric Graph Neural Network
par: Hu, Yuntong, et autres
Publié: (2025)

Space Explanations of Neural Network Classification
par: Labbaf, Faezeh, et autres
Publié: (2025)