Papers Model Optimization
“Model Optimization” 태그가 달린 논문 380편 · 필터 해제
Supervised Learning based QoE Prediction of Video Streaming in Future Networks: A Tutorial with Comparative Study
The Quality of Experience (QoE) based service management remains key for successful provisioning of multimedia services in next-generation networks such as 5G/6G, which requires proper tools for quality monitoring, predi…
Edge-computingFeature EngineeringManagementModel Optimization+1CVNet: Contour Vibration Network for Building Extraction
The classic active contour model raises a great promising solution to polygon-based object extraction with the progress of deep learning recently. Inspired by the physical vibration theory, we propose a contour vibra…
Model OptimizationWeakly Supervised Temporal Sentence Grounding With Gaussian-Based Contrastive Proposal Learning
Temporal sentence grounding aims to detect the most salient moment corresponding to the natural language query from untrimmed videos. As labeling the temporal boundaries is labor-intensive and subjective, the weakly-…
Model OptimizationSentenceTemporal Sentence GroundingTowards Discovering the Effectiveness of Moderately Confident Samples for Semi-Supervised Learning
Semi-supervised learning (SSL) has been studied for a long time to solve vision tasks in data-efficient application scenarios. SSL aims to learn a good classification model using a few labeled data together with larg…
Model OptimizationSemi-Supervised Image ClassificationAn MIL-Derived Transformer for Weakly Supervised Point Cloud Segmentation
We address weakly supervised point cloud segmentation by proposing a new model, MIL-derived transformer, to mine additional supervisory signals. First, the transformer model is derived based on multiple instance lear…
Model OptimizationMultiple Instance LearningPoint Cloud SegmentationE-CRF: Embedded Conditional Random Field for Boundary-caused Class Weights Confusion in Semantic Segmentation
Modern semantic segmentation methods devote much effect to adjusting image feature representations to improve the segmentation performance in various ways, such as architecture design, attention mechnism, etc. However, a…
Metric LearningModel OptimizationSegmentationSemantic SegmentationTechnical Language Supervision for Intelligent Fault Diagnosis in Process Industry
In the process industry, condition monitoring systems with automated fault diagnosis methods assist human experts and thereby improve maintenance efficiency, process sustainability, and workplace safety. Improving the au…
Contrastive LearningFault DiagnosisModel OptimizationTransfer LearningSelectAugment: Hierarchical Deterministic Sample Selection for Data Augmentation
Data augmentation (DA) has been widely investigated to facilitate model optimization in many tasks. However, in most cases, data augmentation is randomly performed for each training sample with a certain probability, whi…
Data AugmentationFine-Grained Image RecognitionHierarchical Reinforcement Learningimage-classification+2Neighborhood Spatial Aggregation MC Dropout for Efficient Uncertainty-aware Semantic Segmentation in Point Clouds
Uncertainty-aware semantic segmentation of the point clouds includes the predictive uncertainty estimation and the uncertainty-guided model optimization. One key challenge in the task is the efficiency of point-wise pred…
Model OptimizationSemantic SegmentationDistilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social Media
Driven by the considerable profits, the crime of drug trafficking (a.k.a. illicit drug trading) has co-evolved with modern technologies, e.g., social media such as Instagram has become a popular platform for marketing an…
Knowledge DistillationMarketingMeta-LearningModel Optimization+1Heterogeneous Language Model Optimization in Automatic Speech Recognition
The rising data privacy risks make it difficult for automatic speech recognition (ASR) systems to acquire complete training data in practical application. Recently, the merge paradigm for acoustic model has been proposed…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Language ModelingLanguage Modelling+4Ultra-Low Power Keyword Spotting at the Edge
Keyword spotting (KWS) has become an indispensable part of many intelligent devices surrounding us, as audio is one of the most efficient ways of interacting with these devices. The accuracy and performance of KWS soluti…
Keyword SpottingModel OptimizationAGGLIO: Global Optimization for Locally Convex Functions
This paper presents AGGLIO (Accelerated Graduated Generalized LInear-model Optimization), a stage-wise, graduated optimization technique that offers global convergence guarantees for non-convex optimization problems whos…
global-optimizationModel OptimizationMultiHead MultiModal Deep Interest Recommendation Network
With the development of information technology, human beings are constantly producing a large amount of information at all times. How to obtain the information that users are interested in from the large amount of inform…
Model OptimizationRecommendation SystemsWinning the ICCV'2021 VALUE Challenge: Task-aware Ensemble and Transfer Learning with Visual Concepts
The VALUE (Video-And-Language Understanding Evaluation) benchmark is newly introduced to evaluate and analyze multi-modal representation learning algorithms on three video-and-language tasks: Retrieval, QA, and Captionin…
Model OptimizationRepresentation LearningRetrievalTransfer Learning$Δ$-UQ: Accurate Uncertainty Quantification via Anchor Marginalization
We present $\Delta$-UQ -- a novel, general-purpose uncertainty estimator using the concept of anchoring in predictive models. Anchoring works by first transforming the input into a tuple consisting of an anchor point dra…
Model OptimizationOut-of-Distribution DetectionUncertainty QuantificationiLQR-VAE : control-based learning of input-driven dynamics with applications to neural data
Understanding how neural dynamics give rise to behaviour is one of the most fundamental questions in systems neuroscience. To achieve this, a common approach is to record neural populations in behaving animals, and model…
Model OptimizationVariational InferenceA Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions
Recommender system is one of the most important information services on today's Internet. Recently, graph neural networks have become the new state-of-the-art approach to recommender systems. In this survey, we conduct a…
graph constructionGraph Neural NetworkModel OptimizationRecommendation SystemsSelf-Supervised Learning for MRI Reconstruction with a Parallel Network Training Framework
Image reconstruction from undersampled k-space data plays an important role in accelerating the acquisition of MR data, and a lot of deep learning-based methods have been exploited recently. Despite the achieved inspirin…
Image ReconstructionModel OptimizationMRI ReconstructionSelf-Supervised LearningLike Article, Like Audience: Enforcing Multimodal Correlations for Disinformation Detection
User-generated content (e.g., tweets and profile descriptions) and shared content between users (e.g., news articles) reflect a user's online identity. This paper investigates whether correlations between user-generated …
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