Artificial Structure Function Search: Preserving Artificial Functional Connectivity for Structured Pruning
cs.LG, cs.AI
Submitted: 2026-09-21
Updated: 2026-09-21
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- SliceGPT: Compress Large Language Models by Deleting Rows and Columns
- The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
- MiniLLM: On-Policy Distillation of Large Language Models
- SlimLLM: Accurate Structured Pruning for Large Language Models
- Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
- One Shot vs. Iterative: Rethinking Pruning Strategies for Model Compression
- SNIP: Single-shot Network Pruning based on Connection Sensitivity
- Rethinking the Value of Network Pruning
- Structure-Aware Automatic Channel Pruning by Searching with Graph Embedding
- Preserving Deep Representations In One-Shot Pruning: A Hessian-Free Second-Order Optimization Framework
- OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Probabilistic Modeling: Proving the Lottery Ticket Hypothesis in Spiking Neural Network
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