12 citations · 14 across the 14 of their papers we have counts for
10 papers · 1 filter
Forward-Free LLM Depth Pruning via Weight Redundancy
Vincent-Daniel Yun, Woosang Lim
Depth pruning reduces large language model (LLM) inference cost by removing complete Transformer blocks. Activation-based methods collect hidden states through forward passes on ca…
Output-Aware Rotation for INT2 KV-Cache Quantization
Vincent-Daniel Yun, Woosang Lim, Minsoo Cheong +4
The key-value (KV) cache has become a major memory and bandwidth bottleneck in long-context large language model inference, making ultra-low-bit quantization increasingly important…
Ghosted Layers: Unconstrained Activation Alignment for Recovering Layer-Pruned LLMs
Vincent-Daniel Yun, Junhyuk Jo, Sai Praneeth Karimireddy +1
Layer pruning removes entire Transformer decoder blocks from large language models, but introduces a mismatch between the hidden state received by the next surviving layer and the…
Rethinking Layer Redundancy: Calibration Matters More Than Search in LLM Depth Pruning
Minkyu Kim, Vincent-Daniel Yun, Youngrae Kim +3
Depth pruning improves the inference efficiency of large language models by removing Transformer blocks. Prior work typically treats layer redundancy as an inherent structural prop…
Locality-Aware Redundancy Pruning for LLM Depth Compression
Vincent-Daniel Yun, Youngrae Kim, Woosang Lim +3
Large language models are known to contain representational redundancy across network depth, making depth pruning an effective approach for improving inference efficiency. Existing…
Weight Concentration Regularization for Improving Pruning Robustness Under High Sparsity
Vincent-Daniel Yun, Junhyuk Jo, Sunwoo Lee
Deep neural networks achieve outstanding performance across vision and language tasks, yet their large parameter counts limit deployment in resource-constrained settings. One-shot…