44 citations · 46 across the 3 of their papers we have counts for
4 papers · 1 filter
Efficient Test-time Adaptation through Candidate Verification and Divergence Shifts
Seungmin Oh, Seunghun Kang, Jongbin Ryu
Vision-language models (VLMs) achieve strong zero-shot transferability but remain vulnerable to target-domain shifts at inference time. Test-time adaptation (TTA) offers a practica…
Differentiable Bit-Widths: Co-optimizing Pruning and Quantization via SVD for Ultra-Efficient LLM Compression
Hankyul Kang, Jongbin Ryu
SVD-based pruning and quantization have recently emerged as a promising strategy for the ultra-efficient compression of large language models. In these methods, compression is perf…
Layer-wise Curriculum Learning for Efficient LLM Compression
Donggeon Lee, Dooyeon Na, Seungmin Oh +1
In this paper, we introduce layer-wise curriculum learning for efficient LLM compression. The proposed method facilitates the knowledge transfer from the teacher model to the stude…
Colla-Q: Toward Collaborative Experts in MoE Quantization via Minimax Precision Balancing
Eunju Shin, Jongbin Ryu
In this paper, we present a Mixture-of-Experts (MoE) quantization method based on activation entropy. Although quantization reduces memory and computational costs, it can substanti…