
DoubleUncertainty Weighted Method for Semisupervised Learning
Though deep learning has achieved advanced performance recently, it rema...
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ModalityPairing Learning for Brain Tumor Segmentation
Automatic brain tumor segmentation from multimodality Magnetic Resonanc...
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GMH: A General Multihop Reasoning Model for KG Completion
Knowledge graphs are essential for numerous downstream natural language ...
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CASTLE: Regularization via Auxiliary Causal Graph Discovery
Regularization improves generalization of supervised models to outofsa...
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Subgraph Contrast for Scalable SelfSupervised Graph Representation Learning
Graph representation learning has attracted lots of attention recently. ...
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WoodpeckerDL: Accelerating Deep Neural Networks via HardwareAware Multifaceted Optimizations
Accelerating deep model training and inference is crucial in practice. E...
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Train Your Data Processor: DistributionAware and ErrorCompensation Coordinate Decoding for Human Pose Estimation
Recently, the leading performance of human pose estimation is dominated ...
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AutoNCP: Automated pipelines for accurate confidence intervals
Successful application of machine learning models to realworld predicti...
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Does NonCOVID19 Lung Lesion Help? Investigating Transferability in COVID19 CT Image Segmentation
Coronavirus disease 2019 (COVID19) is a highly contagious virus spreadi...
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Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification
Subgroup analysis of treatment effects plays an important role in applic...
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Sybilproof Answer Querying Mechanism
We study a question answering problem on a social network, where a reque...
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FineGrained Fashion Similarity Learning by AttributeSpecific Embedding Network
This paper strives to learn finegrained fashion similarity. In this sim...
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Learning Overlapping Representations for the Estimation of Individualized Treatment Effects
The choice of making an intervention depends on its potential benefit or...
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Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning
An essential problem in automated machine learning (AutoML) is that of m...
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Maximal Information Propagation with Budgets
In this work, we present an information propagation game on a network wh...
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The state of the art in kidney and kidney tumor segmentation in contrastenhanced CT imaging: Results of the KiTS19 Challenge
There is a large body of literature linking anatomic and geometric chara...
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Semantic Feature Attention Network for Liver Tumor Segmentation in Largescale CT database
Liver tumor segmentation plays an important role in hepatocellular carci...
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Incentivize Diffusion with Fair Rewards on Networks
This paper studies a sale promotion mechanism design problem on a social...
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Cascaded Volumetric Convolutional Network for Kidney Tumor Segmentation from CT volumes
Automated segmentation of kidney and tumor from 3D CT scans is necessary...
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Lifelong Bayesian Optimization
Automatic Machine Learning (AutoML) systems tackle the problem of autom...
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Crowdsourcing Data Acquisition via Social Networks
We consider a requester who acquires a set of data (e.g. images) that is...
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A MobilityAware Vehicular Caching Scheme in Content Centric Networks: Model and Optimization
Edge caching is being explored as a promising technology to alleviate th...
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Bayesian semisupervised learning for uncertaintycalibrated prediction of molecular properties and active learning
Predicting bioactivity and physical properties of small molecules is a c...
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Geometry of energy landscapes and the optimizability of deep neural networks
Deep neural networks are workhorse models in machine learning with multi...
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Dermoscopic Image Analysis for ISIC Challenge 2018
This short paper reports the algorithms we used and the evaluation perfo...
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Building Transmission Backbone for Highway Vehicular Networks: Framework and Analysis
The highway vehicular ad hoc networks, where vehicles are wirelessly int...
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BigDL: A Distributed Deep Learning Framework for Big Data
In this paper, we present BigDL, a distributed deep learning framework f...
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Energyentropy competition and the effectiveness of stochastic gradient descent in machine learning
Finding parameters that minimise a loss function is at the core of many ...
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Application of Convolutional Neural Network to Predict Airfoil Lift Coefficient
The adaptability of the convolutional neural network (CNN) technique for...
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Distributed Representation of Subgraphs
Network embeddings have become very popular in learning effective featur...
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Yao Zhang
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