Imagen de Portada

A Novel Explanation Against Linear Neural Networks

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autor principal:	Lakkapragada, Anish
Formato:	Preprint
Publicado:	2023
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Using physics-inspired Singular Learning Theory to understand grokking & other phase transitions in modern neural networks
por: Lakkapragada, Anish
Publicado: (2025)

Exponential Family Discriminant Analysis: Generalizing LDA-Style Generative Classification to Non-Gaussian Models
por: Lakkapragada, Anish
Publicado: (2026)

Comprehensive OOD Detection Improvements
por: Lakkapragada, Anish, et al.
Publicado: (2024)

CosmicFish-HRM: Adaptive Reasoning via Hierarchical Recurrent Mechanisms in Compact Language Models
por: Lakkapragada, Venkat Akhil
Publicado: (2026)

SEEN: Sharpening Explanations for Graph Neural Networks using Explanations from Neighborhoods
por: Cho, Hyeoncheol, et al.
Publicado: (2021)

Graph Neural Network Explanations are Fragile
por: Li, Jiate, et al.
Publicado: (2024)

Faster Verified Explanations for Neural Networks
por: De Palma, Alessandro, et al.
Publicado: (2025)

Identifying Backdoored Graphs in Graph Neural Network Training: An Explanation-Based Approach with Novel Metrics
por: Downer, Jane, et al.
Publicado: (2024)

Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors
por: Kadir, Md Abdul, et al.
Publicado: (2024)

View-based Explanations for Graph Neural Networks
por: Chen, Tingyang, et al.
Publicado: (2024)

Is Your Explanation Reliable: Confidence-Aware Explanation on Graph Neural Networks
por: Zhang, Jiaxing, et al.
Publicado: (2025)

Space Explanations of Neural Network Classification
por: Labbaf, Faezeh, et al.
Publicado: (2025)

Ignoring Directionality Leads to Compromised Graph Neural Network Explanations
por: Sun, Changsheng, et al.
Publicado: (2025)

Can Subgraph Explanations Be Weaponized to Steal Graph Neural Networks?
por: Nimase, Ojas, et al.
Publicado: (2026)

The GECo algorithm for Graph Neural Networks Explanation
por: Calderaro, Salvatore, et al.
Publicado: (2024)

Regularizing Explanations in Bayesian Convolutional Neural Networks
por: Bekkemoen, Yanzhe, et al.
Publicado: (2021)

Pathwise Explanation of ReLU Neural Networks
por: Lim, Seongwoo, et al.
Publicado: (2025)

B-SMALL: A Bayesian Neural Network approach to Sparse Model-Agnostic Meta-Learning
por: Madan, Anish, et al.
Publicado: (2021)

GIN-Graph: A Generative Interpretation Network for Model-Level Explanation of Graph Neural Networks
por: Yue, Xiao, et al.
Publicado: (2025)

Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate Models
por: Engel, Andrew, et al.
Publicado: (2023)

Counterfactual Explanations for Hypergraph Neural Networks
por: Veglianti, Fabiano, et al.
Publicado: (2026)

Attribution Explanations for Deep Neural Networks: A Theoretical Perspective
por: Deng, Huiqi, et al.
Publicado: (2025)

A Model of Causal Explanation on Neural Networks for Tabular Data
por: Isozaki, Takashi, et al.
Publicado: (2025)

Counterfactual Explanations for Linear Optimization
por: Kurtz, Jannis, et al.
Publicado: (2024)

Uncertainty-Aware Explanations Through Probabilistic Self-Explainable Neural Networks
por: Vadillo, Jon, et al.
Publicado: (2024)

Explaining Hypergraph Neural Networks: From Local Explanations to Global Concepts
por: Su, Shiye, et al.
Publicado: (2024)

Graph Neural Network Causal Explanation via Neural Causal Models
por: Behnam, Arman, et al.
Publicado: (2024)

Adaptive Local Neighborhood-based Neural Networks for MR Image Reconstruction from Undersampled Data
por: Liang, Shijun, et al.
Publicado: (2022)

Uncertainty Quantification for Gradient-based Explanations in Neural Networks
por: Mulye, Mihir, et al.
Publicado: (2024)

Game-theoretic Counterfactual Explanation for Graph Neural Networks
por: Chhablani, Chirag, et al.
Publicado: (2024)

FAME: Formal Abstract Minimal Explanation for Neural Networks
por: Boumazouza, Ryma, et al.
Publicado: (2026)

Beyond Edge Deletion: A Comprehensive Approach to Counterfactual Explanation in Graph Neural Networks
por: De Sanctis, Matteo, et al.
Publicado: (2026)

How Explanations Leak the Decision Logic: Stealing Graph Neural Networks via Explanation Alignment
por: Ma, Bin, et al.
Publicado: (2025)

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

RAW-Explainer: Post-hoc Explanations of Graph Neural Networks on Knowledge Graphs
por: Kubo, Ryoji, et al.
Publicado: (2025)

Iterative Graph Neural Network Enhancement via Frequent Subgraph Mining of Explanations
por: Naik, Harish G., et al.
Publicado: (2024)

Sparse Explanations of Neural Networks Using Pruned Layer-Wise Relevance Propagation
por: Sarmiento, Paulo Yanez, et al.
Publicado: (2024)

Quantifying Explanation Quality in Graph Neural Networks using Out-of-Distribution Generalization
por: Zhang, Ding, et al.
Publicado: (2026)

Self-explaining Neural Network with Concept-based Explanations for ICU Mortality Prediction
por: Kumar, Sayantan, et al.
Publicado: (2021)

Efficiently Transforming Neural Networks into Decision Trees: A Path to Ground Truth Explanations with RENTT
por: Monke, Helena, et al.
Publicado: (2025)