Imagen de Portada

Using the Path of Least Resistance to Explain Deep Networks

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Salek, Sina, Enguehard, Joseph
Formato:	Preprint
Publicado:	2025
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Anomaly Detection Based on Critical Paths for Deep Neural Networks
por: Zhao, Fangzhen, et al.
Publicado: (2025)

The Effective Horizon Explains Deep RL Performance in Stochastic Environments
por: Laidlaw, Cassidy, et al.
Publicado: (2023)

Using Persuasive Writing Strategies to Explain and Detect Health Misinformation
por: Kamali, Danial, et al.
Publicado: (2022)

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

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

Explaining Neural Networks without Access to Training Data
por: Marton, Sascha, et al.
Publicado: (2022)

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

BET: Explaining Deep Reinforcement Learning through The Error-Prone Decisions
por: Liu, Xiao, et al.
Publicado: (2024)

Explaining Categorical Feature Interactions Using Graph Covariance and LLMs
por: Shen, Cencheng, et al.
Publicado: (2025)

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

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

Random Network Distillation Based Deep Reinforcement Learning for AGV Path Planning
por: Yin, Huilin, et al.
Publicado: (2024)

ARDDQN: Attention Recurrent Double Deep Q-Network for UAV Coverage Path Planning and Data Harvesting
por: Kumar, Praveen, et al.
Publicado: (2024)

Deep Causal Learning to Explain and Quantify The Geo-Tension's Impact on Natural Gas Market
por: Peter, Philipp Kai, et al.
Publicado: (2024)

SVD Based Least Squares for X-Ray Pneumonia Classification Using Deep Features
por: Erdogan, Mete, et al.
Publicado: (2025)

Actionable Counterfactual Explanations Using Bayesian Networks and Path Planning with Applications to Environmental Quality Improvement
por: Valero-Leal, Enrique, et al.
Publicado: (2025)

Compressing Deep Neural Networks Using Explainable AI
por: Soroush, Kimia, et al.
Publicado: (2025)

ABLE: Using Adversarial Pairs to Construct Local Models for Explaining Model Predictions
por: Khadka, Krishna, et al.
Publicado: (2025)

eXpLogic: Explaining Logic Types and Patterns in DiffLogic Networks
por: Wormald, Stephen, et al.
Publicado: (2025)

COOL-MC: Verifying and Explaining RL Policies for Multi-bridge Network Maintenance
por: Gross, Dennis
Publicado: (2026)

Learning by Self-Explaining
por: Stammer, Wolfgang, et al.
Publicado: (2023)

Improving Trip Mode Choice Modeling Using Ensemble Synthesizer (ENSY)
por: Parsi, Amirhossein, et al.
Publicado: (2024)

Transformers Don't In-Context Learn Least Squares Regression
por: Hill, Joshua, et al.
Publicado: (2025)

Progressive Inference: Explaining Decoder-Only Sequence Classification Models Using Intermediate Predictions
por: Kariyappa, Sanjay, et al.
Publicado: (2024)

Explaining Deep Learning-based Anomaly Detection in Energy Consumption Data by Focusing on Contextually Relevant Data
por: Noorchenarboo, Mohammad, et al.
Publicado: (2025)

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

Learning Shortest Paths with Generative Flow Networks
por: Morozov, Nikita, et al.
Publicado: (2026)

Explaining Predictions by Characteristic Rules
por: Alkhatib, Amr, et al.
Publicado: (2024)

Explaining RL Decisions with Trajectories
por: Deshmukh, Shripad Vilasrao, et al.
Publicado: (2023)

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

Tracing the Path to Grokking: Embeddings, Dropout, and Network Activation
por: Salah, Ahmed, et al.
Publicado: (2025)

eXpath: Explaining Knowledge Graph Link Prediction with Ontological Closed Path Rules
por: Sun, Ye, et al.
Publicado: (2024)

Grokking Explained: A Statistical Phenomenon
por: Carvalho, Breno W., et al.
Publicado: (2025)

Solving Pasur Using GPU-Accelerated Counterfactual Regret Minimization
por: Baghal, Sina
Publicado: (2025)

Explaining Drift using Shapley Values
por: Edakunni, Narayanan U., et al.
Publicado: (2024)

Least-Loaded Expert Parallelism: Load Balancing An Imbalanced Mixture-of-Experts
por: Nguyen, Xuan-Phi, et al.
Publicado: (2026)

KernelSHAP-IQ: Weighted Least-Square Optimization for Shapley Interactions
por: Fumagalli, Fabian, et al.
Publicado: (2024)

RandomNet: Clustering Time Series Using Untrained Deep Neural Networks
por: Li, Xiaosheng, et al.
Publicado: (2024)

Comparing Explanations is Not Enough, Explain the Change: New Standards are Needed to Explain Behavioral Shifts in Large Language Models
por: Ciaperoni, Martino, et al.
Publicado: (2026)

FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks
por: Schmalwasser, Laines, et al.
Publicado: (2025)