Papers Model Optimization
“Model Optimization” 태그가 달린 논문 380편 · 필터 해제
Effortless Active Labeling for Long-Term Test-Time Adaptation
Long-term test-time adaptation (TTA) is a challenging task due to error accumulation. Recent approaches tackle this issue by actively labeling a small proportion of samples in each batch, yet the annotation burden qu…
Model OptimizationTest-time AdaptationCGMatch: A Different Perspective of Semi-supervised Learning
Semi-supervised learning (SSL) has garnered significant attention due to its ability to leverage limited labeled data and a large amount of unlabeled data to improve model generalization performance. Recent approache…
Model OptimizationOptimizing Large Language Models with an Enhanced LoRA Fine-Tuning Algorithm for Efficiency and Robustness in NLP Tasks
This study proposes a large language model optimization method based on the improved LoRA fine-tuning algorithm, aiming to improve the accuracy and computational efficiency of the model in natural language processing tas…
Computational EfficiencyLanguage ModelingLanguage ModellingLarge Language Model+3Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain Generalization
Open-Set Domain Generalization (OSDG) is a challenging task requiring models to accurately predict familiar categories while minimizing confidence for unknown categories to effectively reject them in unseen domains. Whil…
DenoisingDomain GeneralizationMeta-LearningModel Optimization+1Efficient Detection Framework Adaptation for Edge Computing: A Plug-and-play Neural Network Toolbox Enabling Edge Deployment
Edge computing has emerged as a key paradigm for deploying deep learning-based object detection in time-sensitive scenarios. However, existing edge detection methods face challenges: 1) difficulty balancing detection pre…
Edge-computingEdge DetectionModel Optimizationobject-detection+1A Deep Reinforcement Learning Framework for Dynamic Portfolio Optimization: Evidence from China's Stock Market
Artificial intelligence is transforming financial investment decision-making frameworks, with deep reinforcement learning demonstrating substantial potential in robo-advisory applications. This paper addresses the limita…
BenchmarkingDecision MakingDeep Reinforcement LearningModel Optimization+3Data-Driven Fairness Generalization for Deepfake Detection
Despite the progress made in deepfake detection research, recent studies have shown that biases in the training data for these detectors can result in varying levels of performance across different demographic groups, su…
DeepFake DetectionFace SwappingFairnessImage Manipulation+3Developing a Dataset-Adaptive, Normalized Metric for Machine Learning Model Assessment: Integrating Size, Complexity, and Class Imbalance
Traditional metrics like accuracy, F1-score, and precision are frequently used to evaluate machine learning models, however they may not be sufficient for evaluating performance on tiny, unbalanced, or high-dimensional d…
Model OptimizationLossless Model Compression via Joint Low-Rank Factorization Optimization
Low-rank factorization is a popular model compression technique that minimizes the error $\delta$ between approximated and original weight matrices. Despite achieving performances close to the original models when $\delt…
Model CompressionModel OptimizationRFSR: Improving ISR Diffusion Models via Reward Feedback Learning
Generative diffusion models (DM) have been extensively utilized in image super-resolution (ISR). Most of the existing methods adopt the denoising loss from DDPMs for model optimization. We posit that introducing reward f…
Blind Super-ResolutionImage Super-ResolutionModel OptimizationSuper-ResolutionBehavior Backdoor for Deep Learning Models
The various post-processing methods for deep-learning-based models, such as quantification, pruning, and fine-tuning, play an increasingly important role in artificial intelligence technology, with pre-train large models…
Backdoor AttackDeep LearningModel OptimizationTowards Data-centric Machine Learning on Directed Graphs: a Survey
In recent years, Graph Neural Networks (GNNs) have made significant advances in processing structured data. However, most of them primarily adopted a model-centric approach, which simplifies graphs by converting them int…
Graph LearningModel OptimizationSurveyOptimized Conformal Selection: Powerful Selective Inference After Conformity Score Optimization
Model selection/optimization in conformal inference is challenging, since it may break the exchangeability between labeled and unlabeled data. We study this problem in the context of conformal selection, which uses confo…
Drug DiscoveryModel OptimizationModel SelectionvalidMMGenBench: Evaluating the Limits of LMMs from the Text-to-Image Generation Perspective
Large Multimodal Models (LMMs) have demonstrated remarkable capabilities. While existing benchmarks for evaluating LMMs mainly focus on image comprehension, few works evaluate them from the image generation perspective. …
Image ComprehensionImage GenerationModel OptimizationText to Image Generation+1Transforming the Hybrid Cloud for Emerging AI Workloads
This white paper, developed through close collaboration between IBM Research and UIUC researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads thr…
Model Optimizationscientific discoveryWeather ForecastingIoT-Based 3D Pose Estimation and Motion Optimization for Athletes: Application of C3D and OpenPose
This study proposes the IoT-Enhanced Pose Optimization Network (IE-PONet) for high-precision 3D pose estimation and motion optimization of track and field athletes. IE-PONet integrates C3D for spatiotemporal feature extr…
3D Pose EstimationBayesian OptimizationData IntegrationKeypoint Detection+2RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively
Deep learning has revolutionized computing in many real-world applications, arguably due to its remarkable performance and extreme convenience as an end-to-end solution. However, deep learning models can be costly to tra…
Deep LearningModel OptimizationNeural Architecture SearchHateful Meme Detection through Context-Sensitive Prompting and Fine-Grained Labeling
The prevalence of multi-modal content on social media complicates automated moderation strategies. This calls for an enhancement in multi-modal classification and a deeper understanding of understated meanings in images …
Model OptimizationMulti-modal ClassificationEnhancing Predictive Maintenance in Mining Mobile Machinery through a TinyML-enabled Hierarchical Inference Network
Mining machinery operating in variable environments faces high wear and unpredictable stress, challenging Predictive Maintenance (PdM). This paper introduces the Edge Sensor Network for Predictive Maintenance (ESN-PdM), …
Anomaly DetectionModel OptimizationAVSS: Layer Importance Evaluation in Large Language Models via Activation Variance-Sparsity Analysis
The evaluation of layer importance in deep learning has been an active area of research, with significant implications for model optimization and interpretability. Recently, large language models (LLMs) have gained promi…
Language ModelingLanguage ModellingLarge Language ModelModel Optimization+3