Papers Model Optimization
“Model Optimization” 태그가 달린 논문 380편 · 필터 해제
Cough-E: A multimodal, privacy-preserving cough detection algorithm for the edge
Continuous cough monitors can greatly aid doctors in home monitoring and treatment of respiratory diseases. Although many algorithms have been proposed, they still face limitations in data privacy and short-term monitori…
Model OptimizationPrivacy PreservingGeneralists vs. Specialists: Evaluating LLMs on Highly-Constrained Biophysical Sequence Optimization Tasks
Although large language models (LLMs) have shown promise in biomolecule optimization problems, they incur heavy computational costs and struggle to satisfy precise constraints. On the other hand, specialized solvers like…
Bilevel OptimizationModel OptimizationTBBC: Predict True Bacteraemia in Blood Cultures via Deep Learning
Bacteraemia, a bloodstream infection with high morbidity and mortality rates, poses significant diagnostic challenges. Accurate diagnosis through blood cultures is resource-intensive. Developing a machine learning model …
Deep LearningDiagnosticModel OptimizationDeep Learning and Machine Learning -- Object Detection and Semantic Segmentation: From Theory to Applications
An in-depth exploration of object detection and semantic segmentation is provided, combining theoretical foundations with practical applications. State-of-the-art advancements in machine learning and deep learning are re…
Deep LearningModel OptimizationObjectobject-detection+2Acoustic Model Optimization over Multiple Data Sources: Merging and Valuation
Due to the rising awareness of privacy protection and the voluminous scale of speech data, it is becoming infeasible for Automatic Speech Recognition (ASR) system developers to train the acoustic model with complete data…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Model Optimizationspeech-recognition+1Retrieval Augmented Diffusion Model for Structure-informed Antibody Design and Optimization
Antibodies are essential proteins responsible for immune responses in organisms, capable of specifically recognizing antigen molecules of pathogens. Recent advances in generative models have significantly enhanced ration…
DenoisingModel OptimizationRetrievalCutting-Edge Detection of Fatigue in Drivers: A Comparative Study of Object Detection Models
This research delves into the development of a fatigue detection system based on modern object detection algorithms, particularly YOLO (You Only Look Once) models, including YOLOv5, YOLOv6, YOLOv7, and YOLOv8. By compari…
Data AugmentationEdge DetectionModel Optimizationobject-detection+1DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception
Document Layout Analysis is crucial for real-world document understanding systems, but it encounters a challenging trade-off between speed and accuracy: multimodal methods leveraging both text and visual features achieve…
Document Layout Analysisdocument understandingModel OptimizationEasyJudge: an Easy-to-use Tool for Comprehensive Response Evaluation of LLMs
Recently, there has been a growing trend of employing large language models (LLMs) to judge the quality of other LLMs. Many studies have adopted closed-source models, mainly using GPT-4 as the evaluator. However, due to …
Language ModelingLanguage ModellingModel OptimizationMUSO: Achieving Exact Machine Unlearning in Over-Parameterized Regimes
Machine unlearning (MU) is to make a well-trained model behave as if it had never been trained on specific data. In today's over-parameterized models, dominated by neural networks, a common approach is to manually relabe…
Machine UnlearningModel OptimizationTransducer Consistency Regularization for Speech to Text Applications
Consistency regularization is a commonly used practice to encourage the model to generate consistent representation from distorted input features and improve model generalization. It shows significant improvement on vari…
Model OptimizationSpeech-to-TextLanguage-Assisted Human Part Motion Learning for Skeleton-Based Temporal Action Segmentation
Skeleton-based Temporal Action Segmentation involves the dense action classification of variable-length skeleton sequences. Current approaches primarily apply graph-based networks to extract framewise, whole-body-level m…
Action ClassificationAction SegmentationModel OptimizationRepresentation Learning+2Revisiting Essential and Nonessential Settings of Evidential Deep Learning
Evidential Deep Learning (EDL) is an emerging method for uncertainty estimation that provides reliable predictive uncertainty in a single forward pass, attracting significant attention. Grounded in subjective logic, EDL …
Common Sense ReasoningDeep LearningModel OptimizationSuicide Phenotyping from Clinical Notes in Safety-Net Psychiatric Hospital Using Multi-Label Classification with Pre-Trained Language Models
Accurate identification and categorization of suicidal events can yield better suicide precautions, reducing operational burden, and improving care quality in high-acuity psychiatric settings. Pre-trained language models…
ClassificationModel OptimizationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONAutoSTF: Decoupled Neural Architecture Search for Cost-Effective Automated Spatio-Temporal Forecasting
Spatio-temporal forecasting is a critical component of various smart city applications, such as transportation optimization, energy management, and socio-economic analysis. Recently, several automated spatio-temporal for…
energy managementModel OptimizationNeural Architecture SearchSpatio-Temporal ForecastingMoner: Motion Correction in Undersampled Radial MRI with Unsupervised Neural Representation
Motion correction (MoCo) in radial MRI is a challenging problem due to the unpredictability of subject's motion. Current state-of-the-art (SOTA) MoCo algorithms often use extensive high-quality MR images to pre-train neu…
Model OptimizationManiNeg: Manifestation-guided Multimodal Pretraining for Mammography Classification
Breast cancer is a significant threat to human health. Contrastive learning has emerged as an effective method to extract critical lesion features from mammograms, thereby offering a potent tool for breast cancer screeni…
ClassificationContrastive LearningModel OptimizationDAP-LED: Learning Degradation-Aware Priors with CLIP for Joint Low-light Enhancement and Deblurring
Autonomous vehicles and robots often struggle with reliable visual perception at night due to the low illumination and motion blur caused by the long exposure time of RGB cameras. Existing methods address this challenge …
Autonomous VehiclesDeblurringDepth EstimationModel OptimizationFew-Shot Class-Incremental Learning with Non-IID Decentralized Data
Few-shot class-incremental learning is crucial for developing scalable and adaptive intelligent systems, as it enables models to acquire new classes with minimal annotated data while safeguarding the previously accumulat…
class-incremental learningClass Incremental LearningFew-Shot Class-Incremental LearningFew-Shot Learning+2RoomDiffusion: A Specialized Diffusion Model in the Interior Design Industry
Recent advancements in text-to-image diffusion models have significantly transformed visual content generation, yet their application in specialized fields such as interior design remains underexplored. In this paper, we…
Model Optimization