Papers Model Optimization
“Model Optimization” 태그가 달린 논문 380편 · 필터 해제
Dynamic Q&A of Clinical Documents with Large Language Models
Electronic health records (EHRs) house crucial patient data in clinical notes. As these notes grow in volume and complexity, manual extraction becomes challenging. This work introduces a natural language interface using …
ChatbotDecision MakingModel OptimizationQuantization+1Analyzing and Mitigating Bias for Vulnerable Classes: Towards Balanced Representation in Dataset
The accuracy and fairness of perception systems in autonomous driving are essential, especially for vulnerable road users such as cyclists, pedestrians, and motorcyclists who face significant risks in urban driving envir…
Autonomous DrivingData AugmentationFairnessModel OptimizationFourCastNeXt: Optimizing FourCastNet Training for Limited Compute
FourCastNeXt is an optimization of FourCastNet - a global machine learning weather forecasting model - that performs with a comparable level of accuracy and can be trained using around 5% of the original FourCastNet comp…
Model OptimizationWeather ForecastingSelf Supervised Vision for Climate Downscaling
Climate change is one of the most critical challenges that our planet is facing today. Rising global temperatures are already bringing noticeable changes to Earth's weather and climate patterns with an increased frequenc…
Model OptimizationArtificial Intelligence for Operations Research: Revolutionizing the Operations Research Process
The rapid advancement of artificial intelligence (AI) techniques has opened up new opportunities to revolutionize various fields, including operations research (OR). This survey paper explores the integration of AI withi…
Decision MakingModel OptimizationPlug-in Diffusion Model for Sequential Recommendation
Pioneering efforts have verified the effectiveness of the diffusion models in exploring the informative uncertainty for recommendation. Considering the difference between recommendation and image synthesis tasks, existin…
Image GenerationmodelModel OptimizationSequential RecommendationLong-Tail Class Incremental Learning via Independent Sub-prototype Construction
Long-tail class incremental learning (LT-CIL) is designed to perpetually acquire novel knowledge from an imbalanced and perpetually evolving data stream while ensuring the retention of previously acquired knowledge. …
class-incremental learningClass Incremental LearningIncremental LearningModel OptimizationImproved Self-Training for Test-Time Adaptation
Test-time adaptation (TTA) is a technique to improve the performance of a pre-trained source model on a target distribution without using any labeled data. However existing self-trained TTA methods often face the cha…
Model OptimizationPseudo LabelTest-time AdaptationParticle-Based Shape Modeling for Arbitrary Regions-of-Interest
Statistical Shape Modeling (SSM) is a quantitative method for analyzing morphological variations in anatomical structures. These analyses often necessitate building models on targeted anatomical regions of interest to fo…
Model OptimizationNoisy Correspondence Learning with Self-Reinforcing Errors Mitigation
Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice. Recently, to alleviate expensive data collection, co-occurring pairs from the Internet are automatically harvested for tra…
Cross-Modal RetrievalCross-modal retrieval with noisy correspondenceMemorizationModel Optimization+1Object-aware Adaptive-Positivity Learning for Audio-Visual Question Answering
This paper focuses on the Audio-Visual Question Answering (AVQA) task that aims to answer questions derived from untrimmed audible videos. To generate accurate answers, an AVQA model is expected to find the most informat…
Audio-visual Question AnsweringAudio-Visual Question Answering (AVQA)Model OptimizationObject+2Towards Fair Graph Federated Learning via Incentive Mechanisms
Graph federated learning (FL) has emerged as a pivotal paradigm enabling multiple agents to collaboratively train a graph model while preserving local data privacy. Yet, current efforts overlook a key issue: agents are s…
FairnessFederated LearningModel OptimizationDifficulty-Focused Contrastive Learning for Knowledge Tracing with a Large Language Model-Based Difficulty Prediction
This paper presents novel techniques for enhancing the performance of knowledge tracing (KT) models by focusing on the crucial factor of question and concept difficulty level. Despite the acknowledged significance of dif…
Contrastive LearningKnowledge TracingLanguage ModelingLanguage Modelling+2LabelCraft: Empowering Short Video Recommendations with Automated Label Crafting
Short video recommendations often face limitations due to the quality of user feedback, which may not accurately depict user interests. To tackle this challenge, a new task has emerged: generating more dependable labels …
Meta-LearningModel OptimizationVision-language Assisted Attribute Learning
Attribute labeling at large scale is typically incomplete and partial, posing significant challenges to model optimization. Existing attribute learning methods often treat the missing labels as negative or simply ignore …
AttributeLanguage ModelingLanguage ModellingMissing Labels+1SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraints
Semantic segmentation of remote sensing imagery plays a pivotal role in extracting precise information for diverse down-stream applications. Recent development of the Segment Anything Model (SAM), an advanced general-pur…
Model OptimizationNovel ConceptsObjectSegmentation+2VSCode: General Visual Salient and Camouflaged Object Detection with 2D Prompt Learning
Salient object detection (SOD) and camouflaged object detection (COD) are related yet distinct binary mapping tasks. These tasks involve multiple modalities, sharing commonalities and unique cues. Existing research often…
DecoderModel Optimizationobject-detectionObject Detection+3PsyEval: A Suite of Mental Health Related Tasks for Evaluating Large Language Models
Evaluating Large Language Models (LLMs) in the mental health domain poses distinct challenged from other domains, given the subtle and highly subjective nature of symptoms that exhibit significant variability among indiv…
Language ModellingLarge Language ModelModel OptimizationGlanceSeg: Real-time microaneurysm lesion segmentation with gaze-map-guided foundation model for early detection of diabetic retinopathy
Early-stage diabetic retinopathy (DR) presents challenges in clinical diagnosis due to inconspicuous and minute microangioma lesions, resulting in limited research in this area. Additionally, the potential of emerging fo…
Continual LearningLesion SegmentationModel OptimizationSegmentationExplicit Foundation Model Optimization with Self-Attentive Feed-Forward Neural Units
Iterative approximation methods using backpropagation enable the optimization of neural networks, but they remain computationally expensive, especially when used at scale. This paper presents an efficient alternative for…
Model Optimization