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Zero Sum SVD: Balancing Loss Sensitivity for Low Rank LLM Compression
Ali Abbasi, Chayne Thrash, Haoran Qin +3
Advances in large language models have driven strong performance across many tasks, but their memory and compute costs still hinder deployment. SVD-based compression reduces storag…
Low-Rank Prehab: Preparing Neural Networks for SVD Compression
Haoran Qin, Shansita Sharma, Ali Abbasi +2
Low-rank approximation methods such as singular value decomposition (SVD) and its variants (e.g., Fisher-weighted SVD, Activation SVD) have recently emerged as effective tools for…
LOTFormer: Doubly-Stochastic Linear Attention via Low-Rank Optimal Transport
Ashkan Shahbazi, Chayne Thrash, Yikun Bai +3
Transformers have proven highly effective across modalities, but standard softmax attention scales quadratically with sequence length, limiting long context modeling. Linear attent…
CovarNav: Machine Unlearning via Model Inversion and Covariance Navigation
Ali Abbasi, Chayne Thrash, Elaheh Akbari +2
The rapid progress of AI, combined with its unprecedented public adoption and the propensity of large neural networks to memorize training data, has given rise to significant data…