Papers Model Compression
“Model Compression” 태그가 달린 논문 1,632편 · 필터 해제
CrossGMN: Graph Metanetworks for Cross-Architecture Weight-Space Transformations
Weight-space networks operate directly on parameters of other neural networks, enabling tasks such as predicting model properties, editing trained models, and generating weights. Weight-space symmetries such as neuron pe…
Knowledge DistillationImage ClassificationModel CompressionPUBG Ally: A Conversational Embodied Agent as an AI Teammate
We introduce PUBG Ally, an embodied agent for PUBG: BATTLEGROUNDS that can reason, act autonomously, and play alongside players as a voice-enabled teammate. Building such a teammate requires combining two difficult capab…
Model CompressionDebias-SparseGPT: Bias-Aware Pruning for Large Language Models
Model compression techniques such as pruning and quantization facilitate the efficient deployment and acceleration of Large Language Models (LLMs). However, recent studies show that weight sparsification methods, such as…
Computational EfficiencyModel CompressionWhen Pruning Meets Interpretability: Preserving Sparse Autoencoder Robustness in LLMs
Sparse autoencoders (SAEs) are widely used to interpret the internal representations of large language models (LLMs), yet their reliability under post-hoc model compression remains poorly understood. We present a systema…
Model CompressionExploring the Performance Frontier of Compact Unified Image Generation Models
We present Swift-Image, a compact unified model for text-to-image generation, single-image editing, and multi-image editing. Our goal is to explore how far a relatively small visual generator can be pushed through system…
Text-to-Image GenerationReinforcement LearningModel CompressionImage EditingKilobyte Models: Neural Networks as a Seed and a Quantized Latent
The cost of storing and transmitting a trained neural network scales with its parameter count, a bottleneck for over-the-air updates, on-device libraries, and other bandwidth-bound deployments. We study an extreme form o…
Model CompressionMemory Efficient Tabular Foundation Models
Tabular Foundation Models, such as TabPFN, have received a large amount of recent attention due to their performance on in-context tabular machine learning tasks, which often exceeds classical baselines. However, practic…
Model CompressionCoCaRS: Correlation Calibration-Based Redundancy Suppression for Heterogeneous Knowledge Distillation
Knowledge distillation (KD) enables a compact student model to learn from a powerful teacher and has become an effective paradigm for model compression. The emergence of diverse model architectures has extended KD from h…
Knowledge DistillationModel CompressionHilbert Operator for Progressive Encoding (HOPE): A Mathematical Framework for Deconstructing Learned Representations in Deep Networks
Deep neural networks encode complex representations, but deconstructing this internal knowledge remains a challenge. Given the link between learning and compression, network compression offers a promising lens to analyze…
Model CompressionC-PTQ: Fisher-weighted Channel-wise Sensitivity for Post-training Quantization of MLLMs
Multimodal large language models (MLLMs) require huge memory and computational costs, which limits their practical deployment. Post-training quantization (PTQ) techniques offer an efficient solution for model compression…
Model CompressionSmartRAG: Native Graph-Based RAG for Mobile Device
Deploying large language models (LLMs) as personal assistants on mobile devices demands privacy, low latency, and offline availability, yet the computational cost of giant models clashes with strict edge-hardware budgets…
Natural QuestionsModel CompressionAdaptive Model Compression (AMC): Saliency-Driven Resource Allocation for Ultra-Low-Power Transformer Inference
Deploying large-scale transformer models on resource-constrained edge devices remains a challenge due to the high energy and memory overhead inherent in static inference, which processes simple and complex tokens with un…
Model CompressionEvoLP: Self-Evolving Latency Predictor for Model Compression in Real-Time Edge Systems
Edge devices are increasingly utilized for deploying deep learning applications on embedded systems. The real-time nature of many applications and the limited resources of edge devices necessitate latency-targeted neural…
Neural Network CompressionModel CompressionBiSCo-LLM: Lookup-Free Binary Spherical Coding for Extreme Low-Bit Large Language Model Compression
Large language models (LLMs) are increasingly constrained by memory capacity, weight bandwidth, and checkpoint storage during deployment. Existing low-bit compression methods mainly follow two directions. Scalar or group…
Model CompressionEdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI
Convolutional neural networks (CNNs) have demonstrated encouraging results in image classification tasks. However, the prohibitive computational cost of CNNs hinders the deployment of CNNs onto resource-constrained embed…
Image ClassificationModel CompressionImage CroppingTrustworthy Machine Learning through the Lens of Combinatorial Optimization: Survey and Research Perspectives
Modern machine learning (ML) increasingly relies on complex models whose behavior is difficult to characterize beyond empirical performance metrics. Across a wide range of tasks, including prediction, generation, and dec…
Explanation GenerationModel CompressionTensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling
Diffusion models offer a powerful framework for sampling from complex probability densities by learning to reverse a noising process. A common approach involves solving for the time-reversed stochastic differential equat…
Model CompressionDisplacement Preserving Relational Distillation for Robust Medical Segmentation
Accurate 3D medical segmentation is limited by anatomical variability and high computational costs. While knowledge distillation (KD) offers a route for model compression, conventional methods often fail to preserve comp…
Knowledge DistillationModel CompressionRethinking Neural Nonlinearity as Gating
Activation functions are considered an essential primitive for neural nonlinearity, i.e., they enable neural networks to serve as universal approximators. In this paper, we show that this nonlinearity can also be achieve…
Model CompressionFrom General Actions to Domain-Specific Monitoring: Prior-Adaptive Transfer for Skeleton-Based Action Recognition
Skeleton-based action recognition models have recently shown strong performance on large-scale benchmarks with general actions. However, directly transferring them to domain-specific tasks e.g., healthcare monitoring, is…
Action RecognitionModel Compression