:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Xiao, Jinying, Li, Ping, Tang, Zhe, Nie, Jie
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	https://arxiv.org/abs/2403.12690
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

SEVEN: Pruning Transformer Model by Reserving Sentinels
di: Xiao, Jinying, et al.
Pubblicazione: (2024)

TED: Accelerate Model Training by Internal Generalization
di: Xiao, Jinying, et al.
Pubblicazione: (2024)

EMP: Enhance Memory in Data Pruning
di: Xiao, Jinying, et al.
Pubblicazione: (2024)

Training-free Graph Neural Networks and the Power of Labels as Features
di: Sato, Ryoma
Pubblicazione: (2024)

DuoGPT: Training-free Dual Sparsity through Activation-aware Pruning in LLMs
di: Yin, Ruokai, et al.
Pubblicazione: (2025)

MosaicDiff: Training-free Structural Pruning for Diffusion Model Acceleration Reflecting Pretraining Dynamics
di: Guo, Bowei, et al.
Pubblicazione: (2025)

Concurrent Training and Layer Pruning of Deep Neural Networks
di: Guenter, Valentin Frank Ingmar, et al.
Pubblicazione: (2024)

CoRE: Enhancing Metacognition with Label-free Self-evaluation in LRMs
di: Li, Haoxi, et al.
Pubblicazione: (2025)

Quantifying Emergence in Neural Networks: Insights from Pruning and Training Dynamics
di: AlShinaifi, Faisal, et al.
Pubblicazione: (2024)

Balanced Edge Pruning for Graph Anomaly Detection with Noisy Labels
di: Wang, Zhu, et al.
Pubblicazione: (2024)

Soft Label Pruning and Quantization for Large-Scale Dataset Distillation
di: Lingao, Xiao, et al.
Pubblicazione: (2026)

Supervised Robustness-preserving Data-free Neural Network Pruning
di: Meng, Mark Huasong, et al.
Pubblicazione: (2022)

Online Training and Pruning of Deep Reinforcement Learning Networks
di: Guenter, Valentin Frank Ingmar, et al.
Pubblicazione: (2025)

Training-free Heterogeneous Model Merging
di: Xu, Zhengqi, et al.
Pubblicazione: (2024)

Training-Free Dataset Pruning for Instance Segmentation
di: Dai, Yalun, et al.
Pubblicazione: (2025)

Post-Training Neural Network Pruning using Graph Curvature
di: Tan, Shuhang, et al.
Pubblicazione: (2026)

Hierarchical Stage-Wise Training of Linked Deep Neural Networks for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi RSSI Fingerprinting
di: Li, Sihao, et al.
Pubblicazione: (2024)

Variance-Based Pruning for Accelerating and Compressing Trained Networks
di: Berisha, Uranik, et al.
Pubblicazione: (2025)

Training-Free Restoration of Pruned Neural Networks
di: Lee, Keonho, et al.
Pubblicazione: (2025)

ResPrune: Text-Conditioned Subspace Reconstruction for Visual Token Pruning in Large Vision-Language Models
di: Li, Xu, et al.
Pubblicazione: (2026)

Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch
di: Wu, Xidong, et al.
Pubblicazione: (2024)

GraphDancer: Training LLMs to Explore and Reason over Graphs via Two-Stage Curriculum Post-Training
di: Bai, Yuyang, et al.
Pubblicazione: (2026)

The Right to be Forgotten in Pruning: Unveil Machine Unlearning on Sparse Models
di: Xiao, Yang, et al.
Pubblicazione: (2025)

QAdaPrune: Adaptive Parameter Pruning For Training Variational Quantum Circuits
di: Kulshrestha, Ankit, et al.
Pubblicazione: (2024)

PAODING: A High-fidelity Data-free Pruning Toolkit for Debloating Pre-trained Neural Networks
di: Meng, Mark Huasong, et al.
Pubblicazione: (2024)

Rethinking Pruning for Backdoor Mitigation: An Optimization Perspective
di: Li, Nan, et al.
Pubblicazione: (2024)

Magnitude-based Neuron Pruning for Backdoor Defens
di: Li, Nan, et al.
Pubblicazione: (2024)

PruneSymNet: A Symbolic Neural Network and Pruning Algorithm for Symbolic Regression
di: Wu, Min, et al.
Pubblicazione: (2024)

Reconstruct the Pruned Model without Any Retraining
di: Wang, Pingjie, et al.
Pubblicazione: (2024)

Explore and Establish Synergistic Effects Between Weight Pruning and Coreset Selection in Neural Network Training
di: Wan, Weilin, et al.
Pubblicazione: (2025)

Is Complexity Required for Neural Network Pruning? A Case Study on Global Magnitude Pruning
di: Gupta, Manas, et al.
Pubblicazione: (2022)

FASP: Fast and Accurate Structured Pruning of Large Language Models
di: Hu, Hanyu, et al.
Pubblicazione: (2025)

A Phone-based Distributed Ambient Temperature Measurement System with An Efficient Label-free Automated Training Strategy
di: Chen, Dayin, et al.
Pubblicazione: (2024)

Outlier Smoothing with Closed-Form Rotations for W4A4 Large Language Model Quantization
di: Xiao, Jinying, et al.
Pubblicazione: (2025)

Fusing Pruned and Backdoored Models: Optimal Transport-based Data-free Backdoor Mitigation
di: Lin, Weilin, et al.
Pubblicazione: (2024)

Label Propagation Training Schemes for Physics-Informed Neural Networks and Gaussian Processes
di: Zhong, Ming, et al.
Pubblicazione: (2024)

Spectral Pruning for Recurrent Neural Networks
di: Furuya, Takashi, et al.
Pubblicazione: (2021)

Condense, Don't Just Prune: Enhancing Efficiency and Performance in MoE Layer Pruning
di: Cao, Mingyu, et al.
Pubblicazione: (2024)

A Theoretical Analysis of Soft-Label vs Hard-Label Training in Neural Networks
di: Mandal, Saptarshi, et al.
Pubblicazione: (2024)

Enhancing Learning with Label Differential Privacy by Vector Approximation
di: Zhao, Puning, et al.
Pubblicazione: (2024)