Papers Model Compression
“Model Compression” 태그가 달린 논문 1,632편 · 필터 해제
FASTNav: Fine-tuned Adaptive Small-language-models Trained for Multi-point Robot Navigation
With the rapid development of large language models (LLM), robots are starting to enjoy the benefits of new interaction methods that large language models bring. Because edge computing fulfills the needs for rapid respon…
Edge-computingModel CompressionRobot NavigationPuppet-CNN: Input-Adaptive Convolutional Neural Networks with Model Compression using Ordinary Differential Equation
Convolutional Neural Network (CNN) has been applied to more and more scenarios due to its excellent performance in many machine learning tasks, especially with deep and complex structures. However, as the network goes de…
Model CompressionWhat Makes a Good Dataset for Knowledge Distillation?
Knowledge distillation (KD) has been a popular and effective method for model compression. One important assumption of KD is that the teacher's original dataset will also be available when training the student. However, …
Continual LearningKnowledge DistillationModel CompressionBridging the Resource Gap: Deploying Advanced Imitation Learning Models onto Affordable Embedded Platforms
Advanced imitation learning with structures like the transformer is increasingly demonstrating its advantages in robotics. However, deploying these large-scale models on embedded platforms remains a major challenge. In t…
Imitation LearningModel CompressionAn exploration of the effect of quantisation on energy consumption and inference time of StarCoder2
This study examines quantisation and pruning strategies to reduce energy consumption in code Large Language Models (LLMs) inference. Using StarCoder2, we observe increased energy demands with quantization due to lower th…
Model CompressionQuantizationRe-Parameterization of Lightweight Transformer for On-Device Speech Emotion Recognition
With the increasing implementation of machine learning models on edge or Internet-of-Things (IoT) devices, deploying advanced models on resource-constrained IoT devices remains challenging. Transformer models, a currentl…
Emotion RecognitionModel CompressionSpeech Emotion RecognitionASER: Activation Smoothing and Error Reconstruction for Large Language Model Quantization
Quantization stands as a pivotal technique for large language model (LLM) serving, yet it poses significant challenges particularly in achieving effective low-bit quantization. The limited numerical mapping makes the qua…
Language ModelingLanguage ModellingLarge Language ModelModel Compression+1Optimizing Traffic Signal Control using High-Dimensional State Representation and Efficient Deep Reinforcement Learning
In reinforcement learning-based (RL-based) traffic signal control (TSC), decisions on the signal timing are made based on the available information on vehicles at a road intersection. This forms the state representation …
Deep Reinforcement LearningModel CompressionTraffic Signal ControlOWLed: Outlier-weighed Layerwise Pruning for Efficient Autonomous Driving Framework
The integration of Large Language Models (LLMs) into autonomous driving systems offers promising enhancements in environmental understanding and decision-making. However, the substantial computational demands of deployin…
Autonomous DrivingDecision MakingModel CompressionFeature Interaction Fusion Self-Distillation Network For CTR Prediction
Click-Through Rate (CTR) prediction plays a vital role in recommender systems, online advertising, and search engines. Most of the current approaches model feature interactions through stacked or parallel structures, wit…
Click-Through Rate PredictionKnowledge DistillationModel CompressionPrediction+2ZipNN: Lossless Compression for AI Models
With the growth of model sizes and the scale of their deployment, their sheer size burdens the infrastructure requiring more network and more storage to accommodate these. While there is a vast model compression literatu…
Model CompressionFrom Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models
Word embeddings and language models have transformed natural language processing (NLP) by facilitating the representation of linguistic elements in continuous vector spaces. This review visits foundational concepts such …
Model CompressionSentenceTopic ModelsWord EmbeddingsChange Is the Only Constant: Dynamic LLM Slicing based on Layer Redundancy
This paper introduces a novel model compression approach through dynamic layer-specific pruning in Large Language Models (LLMs), enhancing the traditional methodology established by SliceGPT. By transitioning from consta…
Model CompressionEfficient Model Compression for Bayesian Neural Networks
Model Compression has drawn much attention within the deep learning community recently. Compressing a dense neural network offers many advantages including lower computation cost, deployability to devices of limited stor…
Deep Learningfeature selectionmodelModel Compression+1ML Research Benchmark
Artificial intelligence agents are increasingly capable of performing complex tasks across various domains. As these agents advance, there is a growing need to accurately measure and benchmark their capabilities, particu…
Model CompressionNavigateEoRA: Training-free Compensation for Compressed LLM with Eigenspace Low-Rank Approximation
In this work, we re-formulate the model compression problem into the customized compensation problem: Given a compressed model, we aim to introduce residual low-rank paths to compensate for compression errors under custo…
ARCMathModel CompressionQuantization+1LLMCBench: Benchmarking Large Language Model Compression for Efficient Deployment
Although large language models (LLMs) have demonstrated their strong intelligence ability, the high demand for computation and storage hinders their practical application. To this end, many model compression techniques a…
BenchmarkingLanguage ModelingLanguage ModellingLarge Language Model+1SWITCH: Studying with Teacher for Knowledge Distillation of Large Language Models
Despite the success of Large Language Models (LLMs), they still face challenges related to high inference costs and memory requirements. To address these issues, Knowledge Distillation (KD) has emerged as a popular metho…
Instruction FollowingKnowledge DistillationModel CompressionA Survey of Small Language Models
Small Language Models (SLMs) have become increasingly important due to their efficiency and performance to perform various language tasks with minimal computational resources, making them ideal for various settings inclu…
BenchmarkingModel CompressionQuantizationSurveyBeware of Calibration Data for Pruning Large Language Models
As large language models (LLMs) are widely applied across various fields, model compression has become increasingly crucial for reducing costs and improving inference efficiency. Post-training pruning is a promising meth…
Model Compression