Papers Model Compression
“Model Compression” 태그가 달린 논문 1,632편 · 필터 해제
Vitality-Aware Compression for Efficient Image-to-Shape Diffusion Transformers
We propose the first compression approach for image-to-shape Diffusion Transformers (DiTs) that substantially reduces model size while preserving geometric fidelity. Despite remarkable progress in 3D shape generation, la…
Model Compression3D GenerationEPTS: Elastic Post-Training Sparsity for Efficient Large Language Model Compression
Post-Training Sparsity (PTS) has emerged as a crucial paradigm for compressing Large Language Models to facilitate efficient deployment on resource-constrained devices. However, existing PTS methodologies are typically c…
Model CompressionLeaking Circuit Secrets: Gradient Leakage Attacks on Graph Neural Networks
As graph neural networks (GNNs) become standard tools for critical tasks in circuit design and analysis, their security and privacy risks require careful attention. Here, we present the first comprehensive evaluation of …
Model CompressionHybrid Compression: Integrating Pruning and Quantization for Optimized Neural Networks
Deep neural networks have witnessed remarkable advancements in recent years and have become integral to various applications. However, alongside these developments, training and deployment of neural network models on emb…
Neural Network CompressionModel CompressionEfficient Network Inference via Hardware-Aware Architecture Search, Model Pruning & Quantization
Embedded global navigation satellite system (GNSS) interference monitoring requires fast and memory-efficient inference to process large volumes of raw in-phase and quadrature (IQ) samples in real time. At the same time,…
Neural Architecture SearchModel CompressionSVD-Surgeon: Optimal Singular-Value Surgery for Large Language Model Compression
Large language models (LLMs) achieve remarkable performance across a wide range of tasks, but their deployment is constrained by substantial memory and compute requirements. Low-rank compression via singular value decomp…
Model CompressionThe Hitchhiker's Guide to Agentic AI: From Foundations to Systems
The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems. The book covers the full stack from first principles to production deployment, organized around a centr…
Reinforcement LearningModel CompressionOn the Expressive Power of Weight Quantization in Large Language Models
In recent years, weight quantization that encodes the learnable parameters of large language models in an $n$-bit format has garnered significant attention due to its potential for model compression and inference acceler…
Model CompressionStreamKL: Fast and Memory-Efficient KL Divergence for Boosting Attention Distillation
Attention distillation, which trains one attention distribution to match another by minimizing their Kullback-Leibler (KL) divergence, is widely used in knowledge distillation, model compression, continual learning, and …
Knowledge DistillationContinual LearningModel CompressionEfficiency-Performance Trade-offs in Neural Speaker Diarization via Structured Pruning and Low-Bit Quantization
Streaming speaker diarization is crucial for time-critical medical dispatch, but deploying it on resource-constrained hardware requires smaller, faster models. Using SIMSAMU, a dataset of simulated medical-dispatch conve…
Speaker DiarizationModel CompressionImproved Knowledge Distillation for Land-Use Image Classification
In the present article, an improved Knowledge Distillation (KD) framework has been proposed for efficient compression of deep convolutional neural networks for land-use image classification task. Motivated by the need to…
Knowledge DistillationImage ClassificationModel CompressionSigma-Branch: Hierarchical Single-Path Network Reconstruction for Dynamic Inference with Reduced Active Parameters
Deploying deep neural networks on memory-constrained edge accelerators is bottlenecked by per-inference off-chip weight transfer rather than computation: the dense network cannot be retained on-chip, and every parameter …
Model CompressionLLMCodec: Adapting Video Codecs for Efficient Weight Compression of Large Language Models
The rapid development of large language models(LLMs) has led to remarkable advances in natural language processing. However, the increasing scale of these models introduces substantial challenges in terms of storage, tra…
Model CompressionKnowledge Distillation for Visual Autoregressive Models
Autoregressive (AR) image generation models are highly expressive but computationally intensive, motivating effective model compression. Knowledge distillation (KD) is a natural approach for model compression and has bee…
Knowledge DistillationModel CompressionImage GenerationTempoVLA: Learning Speed-Controllable Vision-Language-Action Policies
Robot manipulation alternates between low-risk transit phases that call for fast execution and high-risk contact stages that demand slow, precise motion. Yet existing Vision-Language-Action models (VLAs) only inherit a s…
Reinforcement LearningRobot ManipulationModel CompressionWhat Do Students Learn? A Feature-Level Analysis of Dark Knowledge
Knowledge Distillation (KD) is a powerful tool for model compression, yet the precise mechanisms by which student models acquire feature representations remain underexplored. In this work, we analyze student feature lear…
Knowledge DistillationModel CompressionPSViT: A Methodology for Structurally Pruning Spiking Vision Transformers
Spiking Vision Transformer (SViT) models are promising low-power ViT models for solving vision-based tasks with state-of-the-art performance. However, their large sizes limit their deployments for resource-constrained em…
Model CompressionPrimeSVT: An Automated Memory-aware Pruning Framework with Prioritized Compression Policy for Spiking Vision Transformers
The large sizes of Spiking Vision Transformers (SViTs) still hinder their embedded implementation, highlighting the need for model compression. State-of-the-art works compress SViT models through unstructured pruning, wh…
Model CompressionDoes Compression Preserve Uncertainty? A Unified Benchmark for Quantized and Sparse LLMs via Conformal Prediction
Model compression techniques such as quantization and pruning are widely used to reduce the deployment cost of large language models (LLMs), with existing evaluations focusing almost exclusively on accuracy preservation.…
Model CompressionTrust Region On-Policy Distillation
On-Policy Distillation (OPD) is a fundamental technique for efficient post-training of large language models (LLMs), with broad applications in agent learning, multi-task enhancement, and model compression. However, OPD …
Mathematical ReasoningModel CompressionCode Generation