{"total":20,"rows":[{"id":"2307.09488","title":"PLiNIO: A User-Friendly Library of Gradient-based Methods for Complexity-aware DNN Optimization","created":"2023-07-18","cited_by_count":9,"area":null},{"id":"2506.09104","title":"Unifying Block-wise PTQ and Distillation-based QAT for Progressive Quantization toward 2-bit Instruction-Tuned LLMs","created":"2025-06-10","cited_by_count":8,"area":null},{"id":"2312.10588","title":"Post-Training Quantization for Re-parameterization via Coarse & Fine Weight Splitting","created":"2023-12-17","cited_by_count":6,"area":null},{"id":"2504.03717","title":"RaanA: A Fast, Flexible, and Data-Efficient Post-Training Quantization Algorithm","created":"2025-03-29","cited_by_count":5,"area":null},{"id":"2302.07612","title":"Towards Optimal Compression: Joint Pruning and Quantization","created":"2023-02-15","cited_by_count":4,"area":null},{"id":"2307.04535","title":"QBitOpt: Fast and Accurate Bitwidth Reallocation during Training","created":"2023-07-10","cited_by_count":4,"area":null},{"id":"2308.04269","title":"Lossy and Lossless (L$^2$) Post-training Model Size Compression","created":"2023-08-08","cited_by_count":4,"area":null},{"id":"2306.04879","title":"Augmenting Hessians with Inter-Layer Dependencies for Mixed-Precision Post-Training Quantization","created":"2023-06-08","cited_by_count":3,"area":null},{"id":"2407.18267","title":"MCU-MixQ: A HW/SW Co-optimized Mixed-precision Neural Network Design Framework for MCUs","created":"2024-07-17","cited_by_count":2,"area":null},{"id":"2505.05413","title":"DPQ-HD: Post-Training Compression for Ultra-Low Power Hyperdimensional Computing","created":"2025-05-08","cited_by_count":2,"area":null},{"id":"2511.18474","title":"Adaptive Mesh-Quantization for Neural PDE Solvers","created":"2025-11-23","cited_by_count":2,"area":null},{"id":"2604.13440","title":"A KL Lens on Quantization: Fast, Forward-Only Sensitivity for Mixed-Precision SSM-Transformer Models","created":"2026-04-15","cited_by_count":2,"area":null},{"id":"2308.04753","title":"SAfER: Layer-Level Sensitivity Assessment for Efficient and Robust Neural Network Inference","created":"2023-08-09","cited_by_count":1,"area":null},{"id":"2409.20138","title":"Constraint Guided Model Quantization of Neural Networks","created":"2024-09-30","cited_by_count":1,"area":null},{"id":"2507.09616","title":"MLoRQ: Bridging Low-Rank and Quantization for Transformer Compression","created":"2025-07-13","cited_by_count":1,"area":null},{"id":"2601.14549","title":"QMC: Efficient SLM Edge Inference via Outlier-Aware Quantization and Emergent Memories Co-Design","created":"2026-01-21","cited_by_count":1,"area":null},{"id":"2505.13060","title":"Automatic mixed precision for optimizing gained time with constrained loss mean-squared-error based on model partition to sequential sub-graphs","created":"2025-05-19","cited_by_count":0,"area":null},{"id":"2609.24322","title":"The Undetected Damage of Quantization on Retrieval and How to Fix It","created":"2026-09-21","cited_by_count":0,"area":null},{"id":"2609.28262","title":"RAMP: Robust Adaptive Mixed-Precision Quantization for Edge CPU Vision Models","created":"2026-09-23","cited_by_count":0,"area":null},{"id":"2610.09877","title":"Layerwise Error Attribution for Fast and Robust Mixed-Precision Post-Training Quantization","created":"2026-10-07","cited_by_count":0,"area":null}],"next_cursor":null}