Copertina

Explaining Neural Networks without Access to Training Data

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Marton, Sascha, Lüdtke, Stefan, Bartelt, Christian, Tschalzev, Andrej, Stuckenschmidt, Heiner
Natura:	Preprint
Pubblicazione:	2022
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

A Data-Centric Perspective on Evaluating Machine Learning Models for Tabular Data
di: Tschalzev, Andrej, et al.
Pubblicazione: (2024)

GradTree: Learning Axis-Aligned Decision Trees with Gradient Descent
di: Marton, Sascha, et al.
Pubblicazione: (2023)

Enabling Mixed Effects Neural Networks for Diverse, Clustered Data Using Monte Carlo Methods
di: Tschalzev, Andrej, et al.
Pubblicazione: (2024)

GRANDE: Gradient-Based Decision Tree Ensembles for Tabular Data
di: Marton, Sascha, et al.
Pubblicazione: (2023)

Unreflected Use of Tabular Data Repositories Can Undermine Research Quality
di: Tschalzev, Andrej, et al.
Pubblicazione: (2025)

TabPrep: Closing the Feature Engineering Gap in Tabular Benchmarks
di: Tschalzev, Andrej, et al.
Pubblicazione: (2026)

Mitigating Information Loss in Tree-Based Reinforcement Learning via Direct Optimization
di: Marton, Sascha, et al.
Pubblicazione: (2024)

Disentangling Exploration of Large Language Models by Optimal Exploitation
di: Grams, Tim, et al.
Pubblicazione: (2025)

Interpreting Outliers in Time Series Data through Decoding Autoencoder
di: Knab, Patrick, et al.
Pubblicazione: (2024)

Which LIME should I trust? Concepts, Challenges, and Solutions
di: Knab, Patrick, et al.
Pubblicazione: (2025)

Reevaluation of Inductive Link Prediction
di: Ott, Simon, et al.
Pubblicazione: (2024)

PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances
di: Elyasi, Keyvan Amiri, et al.
Pubblicazione: (2024)

Fact Probability Vector Based Goal Recognition
di: Wilken, Nils, et al.
Pubblicazione: (2024)

A*Net and NBFNet Learn Negative Patterns on Knowledge Graphs
di: Betz, Patrick, et al.
Pubblicazione: (2024)

TabArena: A Living Benchmark for Machine Learning on Tabular Data
di: Erickson, Nick, et al.
Pubblicazione: (2025)

Where to Measure: Epistemic Uncertainty-Based Sensor Placement with ConvCNPs
di: Eksen, Feyza, et al.
Pubblicazione: (2025)

Discovering Behavioral Predispositions in Data to Improve Human Activity Recognition
di: Popko, Maximilian, et al.
Pubblicazione: (2022)

Beyond Pixels: Enhancing LIME with Hierarchical Features and Segmentation Foundation Models
di: Knab, Patrick, et al.
Pubblicazione: (2024)

Deep Classifier Mimicry without Data Access
di: Braun, Steven, et al.
Pubblicazione: (2023)

Analytical Uncertainty-Based Loss Weighting in Multi-Task Learning
di: Kirchdorfer, Lukas, et al.
Pubblicazione: (2024)

GraphXAIN: Narratives to Explain Graph Neural Networks
di: Cedro, Mateusz, et al.
Pubblicazione: (2024)

Self-Explaining Hypergraph Neural Networks for Diagnosis Prediction
di: Yu, Leisheng, et al.
Pubblicazione: (2025)

Explaining Graph Neural Networks for Node Similarity on Graphs
di: Daza, Daniel, et al.
Pubblicazione: (2024)

Beyond Either-Or Reasoning: Transduction and Induction as Cooperative Problem-Solving Paradigms
di: Zenkner, Janis, et al.
Pubblicazione: (2025)

DASH: Warm-Starting Neural Network Training in Stationary Settings without Loss of Plasticity
di: Shin, Baekrok, et al.
Pubblicazione: (2024)

CountTRuCoLa: Rule Confidence Learning for Temporal Knowledge Graph Forecasting
di: Gastinger, Julia, et al.
Pubblicazione: (2025)

L2XGNN: Learning to Explain Graph Neural Networks
di: Serra, Giuseppe, et al.
Pubblicazione: (2022)

Explaining the Explainers in Graph Neural Networks: a Comparative Study
di: Longa, Antonio, et al.
Pubblicazione: (2022)

Towards Learning and Explaining Indirect Causal Effects in Neural Networks
di: Reddy, Abbavaram Gowtham, et al.
Pubblicazione: (2023)

Explaining Bayesian Neural Networks
di: Bykov, Kirill, et al.
Pubblicazione: (2021)

Explaining Neural Networks in Preference Learning: a Post-hoc Inductive Logic Programming Approach
di: Fossemò, Daniele, et al.
Pubblicazione: (2026)

Eigenspectrum Analysis of Neural Networks without Aspect Ratio Bias
di: Hu, Yuanzhe, et al.
Pubblicazione: (2025)

EnterpriseBench Corecraft: Training Generalizable Agents on High-Fidelity RL Environments
di: Mehta, Sushant, et al.
Pubblicazione: (2026)

Fast-DataShapley: Neural Modeling for Training Data Valuation
di: Sun, Haifeng, et al.
Pubblicazione: (2025)

Training Neural Networks for Modularity aids Interpretability
di: Golechha, Satvik, et al.
Pubblicazione: (2024)

Z-Error Loss for Training Neural Networks
di: Godin, Guillaume
Pubblicazione: (2025)

Automatic Stability and Recovery for Neural Network Training
di: Or, Barak
Pubblicazione: (2026)

Gradient-Free Training of Quantized Neural Networks
di: Cohen, Noa, et al.
Pubblicazione: (2024)

Energy Consumption in Parallel Neural Network Training
di: Huber, Philipp, et al.
Pubblicazione: (2025)

Can Kernel Methods Explain How the Data Affects Neural Collapse?
di: Kothapalli, Vignesh, et al.
Pubblicazione: (2024)