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72%Highly rated
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NeurIPS 2026MTS AIMWS AIITMOPruning & sparsity

ROCKET: Rapid Optimization via Calibration-guided Knapsack-Enhanced Truncation for Efficient Model Compression

ROCKET is a training-free compression method using knapsack-based layer allocation and calibration-guided sparse factorization to cut model sizes 20-50% with minimal accuracy loss.

Ammar Ali, Baher Mohammad, Denis Makhov, Dmitriy Shopkhoev and 2 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 18 on Hugging Face · Code ★ 25

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8/20 AI panelreviewers recommend it

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AI panel: 8 of 20 reviewers recommend it
lenient 4/5
medium 4/10
strict 0/5
74%Highly rated
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NeurIPS 2026MWS AIMTS AIITMOEfficient inference & serving

COMPOT: Calibration-Optimized Matrix Procrustes Orthogonalization for Transformers Compression

COMPOT proposes training-free transformer compression via orthogonal dictionaries and closed-form sparse factorization with dynamic layer-wise budget allocation. It achieves superior quality-compression trade-offs versus low-rank and sparse baselines and integrates with quantization.

Denis Makhov, Dmitriy Shopkhoev, Magauiya Zhussip, Ammar Ali and 2 more

Sydney Poster Session 6, Thu, Dec 10, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026 · ▲ 9 on Hugging Face

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9/20 AI panelreviewers recommend it

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AI panel: 9 of 20 reviewers recommend it
lenient 5/5
medium 4/10
strict 0/5