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101 |
Neural Processes for Modeling Personalized Vital-Sign Time-Series Data: Data Pre-processing |
Abstract
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102 |
Estimating People Flows and Counting People |
Abstract
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103 |
Continuous Deep Stereo Adaptation |
Abstract
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104 |
Cross-Domain Semantic Segmentation Model with Confidence-and-Refinement Adaptation |
Abstract
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105 |
Estimation of Confidence Using Auxiliary Models |
Abstract
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106 |
An Anchor Free Object Detector for Point Cloud, CenterNet3D |
Abstract
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107 |
Knowledge Graph-Based Recommendations for Biomedical Relation Extraction |
Abstract
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108 |
For crack detection, BARNet stands for Boundary Aware Refinement Network. |
Abstract
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109 |
Deep neural networks are used to automatically detect aortic valve events from cardiac signals from an epicardially placed accelerometer. |
Abstract
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110 |
Deep learning is used to automatically determine the severity of Pectus Excavatum from CT images. |
Abstract
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111 |
3D Unsupervised Partitioning and Representation Learning Using the AutoAtlas Neural Network |
Abstract
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112 |
Bone Age Assessment Using Attention-Guided Discriminative Region Localization and Label Distribution Learning |
Abstract
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113 |
Dataset reasoning, analysis, and modeling focus |
Abstract
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114 |
Networks of Attention for Person Retrieval |
Abstract
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115 |
Deep Architectures are used to evaluate the severity of Parkinson's disease from videos. |
Abstract
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116 |
Colonoscopy Artificial Intelligence: Past, Present, and Future |
Abstract
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117 |
Analysis of Cross-Domain Datasets Classifier Training on Synthetic Data |
Abstract
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118 |
An Explicit Transformer-Based Deep Learning Model for Heart Failure Incident Prediction |
Abstract
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119 |
A Patch Label Inference Network with Iterative Optimization for Automatic Pavement Distress Detection |
Abstract
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120 |
An End-to-End Multi-Task Learning Model with Edge Refinement and Geometric Deformation for Detecting Driveable Roads |
Abstract
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121 |
Model Design, Experimental Frameworks, Challenges, and Research Needs: An Empirical Review of Deep Learning Frameworks for Change Detection |
Abstract
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122 |
Evaluation of Autonomous Vehicles Under Adversary Conditions in Lane-Change Situations |
Abstract
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123 |
AdaPool: A Model-Free Deep Reinforcement Learning Framework for Diurnal Adaptive Fleet Management with Change Point Detection |
Abstract
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124 |
Deep neural network-based acoustic screening for obstructive sleep apnea in residential settings |
Abstract
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125 |
A Time-Series Feature-Based Recursive Classification Model to Maximize Treatment Approaches for Improving COVID-19 Patients' Outcomes and Resource Allocations |
Abstract
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126 |
A Self-Supervised Gait Encoding Approach for 3D Skeleton-Based Person Re-Identification with Locality Awareness |
Abstract
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127 |
Traffic Prediction Using Multi-Stream Feature Fusion |
Abstract
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128 |
Using structural MRI images, a multi-stream convolutional neural network can classify progressive MCI in Alzheimer's disease. |
Abstract
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129 |
A Multi-Scale Attributes Attention Model for Identification of Transport Modes |
Abstract
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130 |
A Multi-Sensor Traffic Flow Forecasting Framework for Temporal Attention Based on Graphs |
Abstract
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131 |
A Bidirectional Unidirectional Graph Convolutional Stacked LSTM Neural Network for Metro Ridership Prediction |
Abstract
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132 |
A Multi-hop Ride-sharing Distributed Model-Free Algorithm Using Deep Reinforcement Learning |
Abstract
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