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Dmitriy Shopkhoev

2 accepted papers

2026

Share Your Attention: Transformer Weight Sharing via Matrix-based Dictionary Learning

AAAI 2026technical

Large language models (LLMs) have revolutionized AI applications, yet their high computational and memory demands hinder their widespread deployment. Existing compression techniques focus on intra-block optimizations (e.g., low-rank approximation or attention head pruning), while the repetitive laye

Cited by 0SourcePDFScholar
2025

ReplaceMe: Network Simplification via Depth Pruning and Transformer Block Linearization

NeurIPS 2025poster

We introduce ReplaceMe, a generalized training-free depth pruning method that effectively replaces transformer blocks with a linear operation, while maintaining high performance for low compression ratios. In contrast to conventional pruning approaches that require additional training or fine-tuning…

Cited by 0SourcecodeScholar