weight compression

Bi-VLM: Binary Post-Training Quantization for Vision-Language Models AAAI 2026 FLAT-LLM: Fine-grained Low-rank Activation Space Transformation for Large Language Model Compression EACL 2026 PocketLLM: Ultimate Compression of Large Language Models via Meta Networks AAAI 2026 GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference IJCNLP 2025 LVLM-Compress-Bench: Benchmarking the Broader Impact of Large Vision-Language Model Compression NAACL 2025 MCNC: Manifold-Constrained Reparameterization for Neural Compression ICLR 2025 ProcrustesGPT: Compressing LLMs with Structured Matrices and Orthogonal Transformations ACL 2025 SeedLM: Compressing LLM Weights into Seeds of Pseudo-Random Generators ICLR 2025 SSVQ: Unleashing the Potential of Vector Quantization with Sign-Splitting ICCV 2025 Variable Layerwise Quantization: A Simple and Effective Approach to Quantize LLMs ACL 2025 QBB: Quantization with Binary Bases for LLMs NeurIPS 2024 SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression ICLR 2024 Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters ICLR 2019 Learning Compressed Transforms with Low Displacement Rank NeurIPS 2018 “Learning-Compression” Algorithms for Neural Net Pruning CVPR 2018