Imagen de Portada

Attribution-Based Neuron Utility for Plasticity Restoration in Deep Networks

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Elisii, Patrick, Beauchemin, Lucas, Jamshed, Dawer
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Neuron-level Balance between Stability and Plasticity in Deep Reinforcement Learning
por: Lan, Jiahua, et al.
Publicado: (2025)

Silent Neuron Theory and Plasticity Preservation for Deep Reinforcement Learning in Adaptive Video Streaming
por: He, Zhiqiang, et al.
Publicado: (2025)

SNAP: Stopping Catastrophic Forgetting in Hebbian Learning with Sigmoidal Neuronal Adaptive Plasticity
por: Xu, Tianyi, et al.
Publicado: (2024)

Continuous-Utility Direct Preference Optimization
por: Mohsin, Muhammad Ahmed, et al.
Publicado: (2026)

On Measuring Intrinsic Causal Attributions in Deep Neural Networks
por: Saha, Saptarshi, et al.
Publicado: (2025)

Neurons Speak in Ranges: Breaking Free from Discrete Neuronal Attribution
por: Haider, Muhammad Umair, et al.
Publicado: (2025)

NeuronTune: Fine-Grained Neuron Modulation for Balanced Safety-Utility Alignment in LLMs
por: Pan, Birong, et al.
Publicado: (2025)

Neuron-Level Knowledge Attribution in Large Language Models
por: Yu, Zeping, et al.
Publicado: (2023)

NEAT: Concept driven Neuron Attribution in LLMs
por: Kavuri, Vivek Hruday, et al.
Publicado: (2025)

Maintaining Plasticity in Deep Continual Learning
por: Dohare, Shibhansh, et al.
Publicado: (2023)

Plastic Learning with Deep Fourier Features
por: Lewandowski, Alex, et al.
Publicado: (2024)

Machine Unlearning for Robust DNNs: Attribution-Guided Partitioning and Neuron Pruning in Noisy Environments
por: Jin, Deliang, et al.
Publicado: (2025)

Interpreting Neurons in Deep Vision Networks with Language Models
por: Bai, Nicholas, et al.
Publicado: (2024)

Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language Understanding
por: Zhu, Yunchang, et al.
Publicado: (2023)

Multivector Neurons: Better and Faster O(n)-Equivariant Clifford Graph Neural Networks
por: Liu, Cong, et al.
Publicado: (2024)

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

Higher-order Structure Based Anomaly Detection on Attributed Networks
por: Yuan, Xu, et al.
Publicado: (2024)

Rethinking Plasticity in Deep Reinforcement Learning
por: He, Zhiqiang
Publicado: (2026)

Image Restoration Using Deep Regulated Convolutional Networks
por: Liu, Peng, et al.
Publicado: (2019)

Deep Multi-Objective Reinforcement Learning for Utility-Based Infrastructural Maintenance Optimization
por: van Remmerden, Jesse, et al.
Publicado: (2024)

Disentangling the Causes of Plasticity Loss in Neural Networks
por: Lyle, Clare, et al.
Publicado: (2024)

Predicting Plasticity in Deep Continual Learning: A Theoretical Perspective
por: Wang, Jiuqi, et al.
Publicado: (2026)

Scalable Deep Metric Learning on Attributed Graphs
por: Li, Xiang, et al.
Publicado: (2024)

Training Neural Networks by Optimizing Neuron Positions
por: Erb, Laura, et al.
Publicado: (2025)

Disparate Privacy Vulnerability: Targeted Attribute Inference Attacks and Defenses
por: Kabir, Ehsanul, et al.
Publicado: (2025)

Natural Geometry of Robust Data Attribution: From Convex Models to Deep Networks
por: Li, Shihao, et al.
Publicado: (2025)

Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learning
por: Vamplew, Peter, et al.
Publicado: (2024)

On Accelerating Deep Neural Network Mutation Analysis by Neuron and Mutant Clustering
por: Lyons, Lauren, et al.
Publicado: (2025)

Toward Efficient Deep Spiking Neuron Networks:A Survey On Compression
por: Xie, Hui, et al.
Publicado: (2024)

Benchmarking Attribute Discrimination in Infant-Scale Vision-Language Models
por: Batsell, Patrick, et al.
Publicado: (2025)

On the Road with 16 Neurons: Mental Imagery with Bio-inspired Deep Neural Networks
por: Plebe, Alice, et al.
Publicado: (2020)

Neural Network Plasticity and Loss Sharpness
por: Koster, Max, et al.
Publicado: (2024)

A Deep Learning Based Resource Allocator for Communication Networks with Dynamic User Utility Demands
por: Behmandpoor, Pourya, et al.
Publicado: (2023)

Plasticity Loss in Deep Reinforcement Learning: A Survey
por: Klein, Timo, et al.
Publicado: (2024)

Learning Neural Networks by Neuron Pursuit
por: Kumar, Akshay, et al.
Publicado: (2025)

SPHERE: Mitigating the Loss of Spectral Plasticity in Mixture-of-Experts for Deep Reinforcement Learning
por: Luo, Lirui, et al.
Publicado: (2026)

Who Does What in Deep Learning? Multidimensional Game-Theoretic Attribution of Function of Neural Units
por: Dixit, Shrey, et al.
Publicado: (2025)

Deep Reinforcement Learning for Ranking Utility Tuning in the Ad Recommender System at Pinterest
por: Yang, Xiao, et al.
Publicado: (2025)

Rethinking BPS: A Utility-Based Evaluation Framework
por: Özdemir, Konrad, et al.
Publicado: (2025)

Recovering Plasticity of Neural Networks via Soft Weight Rescaling
por: Oh, Seungwon, et al.
Publicado: (2025)