Image de couverture de livre

Concept frustration: Aligning human concepts and machine representations

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Parisini, Enrico, Soelistyo, Christopher J., Isaac, Ahab, Barp, Alessandro, Banerji, Christopher R. S.
Format:	Preprint
Publié:	2026
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Leakage and Interpretability in Concept-Based Models
par: Parisini, Enrico, et autres
Publié: (2025)

Discovering interpretable models of scientific image data with deep learning
par: Soelistyo, Christopher J., et autres
Publié: (2024)

TRESTLE: A Model of Concept Formation in Structured Domains
par: MacLellan, Christopher J., et autres
Publié: (2024)

Addressing divergent representations from causal interventions on neural networks
par: Grant, Satchel, et autres
Publié: (2025)

Explaining Robustness to Catastrophic Forgetting Through Incremental Concept Formation
par: Barari, Nicki, et autres
Publié: (2025)

Causality in the human niche: lessons for machine learning
par: Lange, Richard D., et autres
Publié: (2025)

Explanatory machine learning for sequential human teaching
par: Ai, Lun, et autres
Publié: (2022)

Structure-Aligned Protein Language Model
par: Chen, Can, et autres
Publié: (2025)

Optimal rates for density and mode estimation with expand-and-sparsify representations
par: Sinha, Kaushik, et autres
Publié: (2026)

Concept Alignment
par: Rane, Sunayana, et autres
Publié: (2024)

The Artificial Intelligence Ontology: LLM-assisted construction of AI concept hierarchies
par: Joachimiak, Marcin P., et autres
Publié: (2024)

Analyzing Latent Concepts in Code Language Models
par: Sharma, Arushi, et autres
Publié: (2025)

Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Models
par: Wang, Zhenzhong, et autres
Publié: (2024)

Conceptualizing Uncertainty: A Concept-based Approach to Explaining Uncertainty
par: Roberts, Isaac, et autres
Publié: (2025)

RCCDA: Adaptive Model Updates in the Presence of Concept Drift under a Constrained Resource Budget
par: Piaseczny, Adam, et autres
Publié: (2025)

Aligning LLM agents with human learning and adjustment behavior: a dual agent approach
par: Liu, Tianming, et autres
Publié: (2025)

Generating crossmodal gene expression from cancer histopathology improves multimodal AI predictions
par: Dey, Samiran, et autres
Publié: (2025)

Redundancy and Concept Analysis for Code-trained Language Models
par: Sharma, Arushi, et autres
Publié: (2023)

Deep Learning as Ricci Flow
par: Baptista, Anthony, et autres
Publié: (2024)

Beyond Individual Facts: Investigating Categorical Knowledge Locality of Taxonomy and Meronomy Concepts in GPT Models
par: Burger, Christopher, et autres
Publié: (2024)

Compositional learning of functions in humans and machines
par: Zhou, Yanli, et autres
Publié: (2024)

Provably Accurate Shapley Value Estimation via Leverage Score Sampling
par: Musco, Christopher, et autres
Publié: (2024)

Evidence for Limited Metacognition in LLMs
par: Ackerman, Christopher
Publié: (2025)

Toward using GANs in astrophysical Monte-Carlo simulations
par: Isaac, Ahab, et autres
Publié: (2024)

Active Rule Mining for Multivariate Anomaly Detection in Radio Access Networks
par: Isaac, Ebenezer R. H. P., et autres
Publié: (2025)

Discover physical concepts and equations with machine learning
par: Li, Bao-Bing, et autres
Publié: (2024)

Gradients: When Markets Meet Fine-tuning -- A Distributed Approach to Model Optimisation
par: Subia-Waud, Christopher
Publié: (2025)

Enumerating Safe Regions in Deep Neural Networks with Provable Probabilistic Guarantees
par: Marzari, Luca, et autres
Publié: (2023)

Knowledge graphs for empirical concept retrieval
par: Tětková, Lenka, et autres
Publié: (2024)

PolySHAP: Extending KernelSHAP with Interaction-Informed Polynomial Regression
par: Fumagalli, Fabian, et autres
Publié: (2026)

Regression-adjusted Monte Carlo Estimators for Shapley Values and Probabilistic Values
par: Witter, R. Teal, et autres
Publié: (2025)

Exposing propaganda: an analysis of stylistic cues comparing human annotations and machine classification
par: Faye, Géraud, et autres
Publié: (2024)

Predicting human decisions with behavioral theories and machine learning
par: Plonsky, Ori, et autres
Publié: (2019)

Bayesian Program Learning by Decompiling Amortized Knowledge
par: Palmarini, Alessandro B., et autres
Publié: (2023)

Multimodal Survival Analysis with Locally Deployable Large Language Models
par: Gögl, Moritz, et autres
Publié: (2026)

Bootstrapping-based Regularisation for Reducing Individual Prediction Instability in Clinical Risk Prediction Models
par: Matijevic, Sara, et autres
Publié: (2026)

Var-JEPA: A Variational Formulation of the Joint-Embedding Predictive Architecture -- Bridging Predictive and Generative Self-Supervised Learning
par: Gögl, Moritz, et autres
Publié: (2026)

Cyborg Data: Merging Human with AI Generated Training Data
par: North, Kai, et autres
Publié: (2025)

Principled Data Augmentation for Learning to Solve Quadratic Programming Problems
par: Qian, Chendi, et autres
Publié: (2025)

Decorrelative Network Architecture for Robust Electrocardiogram Classification
par: Wiedeman, Christopher, et autres
Publié: (2022)