Papers Model Compression
“Model Compression” 태그가 달린 논문 1,632편 · 필터 해제
AA-SVD : Anchored and Adaptive SVD for Large Language Model Compression
We introduce a fast low-rank factorization-based framework for compressing large language models that enables rapid compression of billion-parameter models without retraining. Unlike existing factorization-based approach…
Model CompressionAdaLoRA-QAT: Adaptive Low-Rank and Quantization-Aware Segmentation
Chest X-ray (CXR) segmentation is an important step in computer-aided diagnosis, yet deploying large foundation models in clinical settings remains challenging due to computational constraints. We propose AdaLoRA-QAT, a …
Medical Image SegmentationModel CompressionPosition Paper: From Edge AI to Adaptive Edge AI
Edge AI is often framed as model compression and deployment under tight constraints. We argue a stronger operational thesis: Edge AI in realistic deployments is necessarily adaptive. In long-horizon operation, a fixed (n…
Model CompressionBig2Small: A Unifying Neural Network Framework for Model Compression
With the development of foundational models, model compression has become a critical requirement. Various model compression approaches have been proposed such as low-rank decomposition, pruning, quantization, ergodic dyn…
Knowledge DistillationImage ClassificationModel CompressionOneComp: One-Line Revolution for Generative AI Model Compression
Deploying foundation models is increasingly constrained by memory footprint, latency, and hardware costs. Post-training compression can mitigate these bottlenecks by reducing the precision of model parameters without sig…
Model CompressionENEC: A Lossless AI Model Compression Method Enabling Fast Inference on Ascend NPUs
The rapid scaling of Large Language Models presents significant challenges for their deployment and inference, particularly on resource-constrained specialized AI hardware accelerators such as Huawei's Ascend NPUs, where…
Model CompressionDecompose, Mix, Adapt: A Unified Framework for Parameter-Efficient Neural Network Recombination and Compression
Parameter Recombination (PR) methods aim to efficiently compose the weights of a neural network for applications like Parameter-Efficient FineTuning (PEFT) and Model Compression (MC), among others. Most methods typically…
Model CompressionPQuantML: A Tool for End-to-End Hardware-aware Model Compression
PQuantML is a new open-source, hardware-aware neural network model compression library tailored to end-to-end workflows. Motivated by the need to deploy performant models to environments with strict latency constraints, …
Model CompressionJet TaggingA Heterogeneous Long-Micro Scale Cascading Architecture for General Aviation Health Management
BACKGROUND: General aviation fleet expansion demands intelligent health monitoring under computational constraints. Real-world aircraft health diagnosis requires balancing accuracy with computational constraints under ex…
Knowledge DistillationModel CompressionAnomaly DetectionDemystifying Low-Rank Knowledge Distillation in Large Language Models: Convergence, Generalization, and Information-Theoretic Guarantees
Knowledge distillation has emerged as a powerful technique for compressing large language models (LLMs) into efficient, deployable architectures while preserving their advanced capabilities. Recent advances in low-rank k…
Knowledge DistillationModel CompressionPrune-then-Quantize or Quantize-then-Prune? Understanding the Impact of Compression Order in Joint Model Compression
What happens when multiple compression methods are combined-does the order in which they are applied matter? Joint model compression has emerged as a powerful strategy to achieve higher efficiency by combining multiple m…
Model CompressionFrom Inference Efficiency to Embodied Efficiency: Revisiting Efficiency Metrics for Vision-Language-Action Models
Vision-Language-Action (VLA) models have recently enabled embodied agents to perform increasingly complex tasks by jointly reasoning over visual, linguistic, and motor modalities. However, we find that the prevailing not…
Model CompressionZipServ: Fast and Memory-Efficient LLM Inference with Hardware-Aware Lossless Compression
Lossless model compression holds tremendous promise for alleviating the memory and bandwidth bottlenecks in bit-exact Large Language Model (LLM) serving. However, existing approaches often result in substantial inference…
Model CompressionOnly relative ranks matter in weight-clustered large language models
Large language models (LLMs) contain billions of parameters, yet many exact values are not essential. We show that what matters most is the relative rank of weights-whether one connection is stronger or weaker than anoth…
Model CompressionFrequency Matters: Fast Model-Agnostic Data Curation for Pruning and Quantization
Post-training model compression is essential for enhancing the portability of Large Language Models (LLMs) while preserving their performance. While several compression approaches have been proposed, less emphasis has be…
Model CompressionCapability-Guided Compression: Toward Interpretability-Aware Budget Allocation for Large Language Models
Large language model compression has made substantial progress through pruning, quantization, and low-rank decomposition, yet a fundamental limitation persists across all existing methods: compression budgets are allocat…
Model CompressionTinyGLASS: Real-Time Self-Supervised In-Sensor Anomaly Detection
Anomaly detection plays a key role in industrial quality control, where defects must be identified despite the scarcity of labeled faulty samples. Recent self-supervised approaches, such as GLASS, learn normal visual pat…
Model CompressionAnomaly DetectionLatent Semantic Manifolds in Large Language Models
Large Language Models (LLMs) perform internal computations in continuous vector spaces yet produce discrete tokens -- a fundamental mismatch whose geometric consequences remain poorly understood. We develop a mathematica…
Model CompressionSimCert: Probabilistic Certification for Behavioral Similarity in Deep Neural Network Compression
Deploying Deep Neural Networks (DNNs) on resource-constrained embedded systems requires aggressive model compression techniques like quantization and pruning. However, ensuring that the compressed model preserves the beh…
Neural Network CompressionModel CompressionTabKD: Tabular Knowledge Distillation through Interaction Diversity of Learned Feature Bins
Data-free knowledge distillation enables model compression without original training data, critical for privacy-sensitive tabular domains. However, existing methods does not perform well on tabular data because they do n…
Data-free Knowledge DistillationModel CompressionModel extraction