:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Panangat, Aditya
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2505.09864
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

An Efficient Sparse Fine-Tuning with Low Quantization Error via Neural Network Pruning
by: Li, Cen-Jhih, et al.
Published: (2025)

MPruner: Optimizing Neural Network Size with CKA-Based Mutual Information Pruning
by: Hu, Seungbeom, et al.
Published: (2024)

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

A Granger-Causal Perspective on Gradient Descent with Application to Pruning
by: Shah, Aditya, et al.
Published: (2024)

Pruning and Quantization Impact on Graph Neural Networks
by: Khedri, Khatoon, et al.
Published: (2025)

Mutual Information Preserving Neural Network Pruning
by: Westphal, Charles, et al.
Published: (2024)

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

Robust Neural Pruning with Gradient Sampling Optimization for Residual Neural Networks
by: Yun, Juyoung
Published: (2023)

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

Shapley Pruning for Neural Network Compression
by: Adamczewski, Kamil, et al.
Published: (2024)

Exploring Vision Neural Network Pruning via Screening Methodology
by: Wang, Mingyuan, et al.
Published: (2025)

FALCON: FLOP-Aware Combinatorial Optimization for Neural Network Pruning
by: Meng, Xiang, et al.
Published: (2024)

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

ICE-Pruning: An Iterative Cost-Efficient Pruning Pipeline for Deep Neural Networks
by: Hu, Wenhao, et al.
Published: (2025)

Pruning Deep Convolutional Neural Network Using Conditional Mutual Information
by: Vu-Van, Tien, et al.
Published: (2024)

EntryPrune: Neural Network Feature Selection using First Impressions
by: Zimmer, Felix, et al.
Published: (2024)

Adaptive Structured Pruning of Convolutional Neural Networks for Time Series Classification
by: Abdullayev, Javidan, et al.
Published: (2026)

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

Exchangeability in Neural Network and its Application to Dynamic Pruning
by: Pu, et al.
Published: (2025)

Unified Stochastic Framework for Neural Network Quantization and Pruning
by: Zhang, Haoyu, et al.
Published: (2024)

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

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

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

Growing Winning Subnetworks, Not Pruning Them: A Paradigm for Density Discovery in Sparse Neural Networks
by: Yao, Qihang, et al.
Published: (2025)

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

Spectral Theory for Edge Pruning in Asynchronous Recurrent Graph Neural Networks
by: Bessone, Nicolas
Published: (2025)

Budgeted Broadcast: An Activity-Dependent Pruning Rule for Neural Network Efficiency
by: Meirovitch, Yaron, et al.
Published: (2025)

Lattice-Based Pruning in Recurrent Neural Networks via Poset Modeling
by: Sengupta, Rakesh
Published: (2025)

Hierarchical Attention-based Graph Neural Network with Relevance-driven Pruning
by: Kum, Seungwoo
Published: (2026)

The Neural Pruning Law Hypothesis
by: Barbulescu, Eugen, et al.
Published: (2025)

Pruning Deep Neural Networks via a Combination of the Marchenko-Pastur Distribution and Regularization
by: Berlyand, Leonid, et al.
Published: (2025)

Fast Track to Winning Tickets: Repowering One-Shot Pruning for Graph Neural Networks
by: Yue, Yanwei, et al.
Published: (2024)

Joint Pruning and Channel-wise Mixed-Precision Quantization for Efficient Deep Neural Networks
by: Motetti, Beatrice Alessandra, et al.
Published: (2024)

AnoGAN for Tabular Data: A Novel Approach to Anomaly Detection
by: Singh, Aditya, et al.
Published: (2024)

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

A Three-regime Model of Network Pruning
by: Zhou, Yefan, et al.
Published: (2023)

A Novel Explanation Against Linear Neural Networks
by: Lakkapragada, Anish
Published: (2023)

A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks
by: Chakraborty, Biswadeep, et al.
Published: (2024)

Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion
by: Kotecha, Prakrut, et al.
Published: (2025)

Inverted Activations: Reducing Memory Footprint in Neural Network Training
by: Novikov, Georgii, et al.
Published: (2024)