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Found 10 papers, 5 papers with code
A Knowledge Graph Perspective on Supply Chain Resilience
Global crises and regulatory developments require increased supply chain transparency and resilience.
On Calibration of Graph Neural Networks for Node Classification
We investigate the calibration of graph neural networks for node classification, study the effect of existing post-processing calibration methods, and analyze the influence of model capacity, graph density, and a new loss function on calibration.
TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs
Conventional static knowledge graphs model entities in relational data as nodes, connected by edges of specific relation types.
Cascaded Channel Estimation for RIS Assisted mmWave MIMO Transmissions
Channel estimation is challenging for the reconfigurable intelligence surface (RIS) assisted millimeter wave (mmWave) communications.
Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphs
Biomedical knowledge graphs permit an integrative computational approach to reasoning about biological systems.
Deep Learning based Channel Extrapolation for Large-Scale Antenna Systems: Opportunities, Challenges and Solutions
With the depletion of spectrum, wireless communication systems turn to exploit large antenna arrays to achieve the degree of freedom in space domain, such as millimeter wave massive multi-input multioutput (MIMO), reconfigurable intelligent surface assisted communications and cell-free massive MIMO.
Information Theory Signal Processing Information Theory
TIMELY: Improving Labeling Consistency in Medical Imaging for Cell Type Classification
Diagnosing diseases such as leukemia or anemia requires reliable counts of blood cells.
Integrating Logical Rules Into Neural Multi-Hop Reasoning for Drug Repurposing
The graph structure of biomedical data differs from those in typical knowledge graph benchmark tasks.
Learning Individualized Treatment Rules with Estimated Translated Inverse Propensity Score
Randomized controlled trials typically analyze the effectiveness of treatments with the goal of making treatment recommendations for patient subgroups.
Adversarial Examples Versus Cloud-based Detectors: A Black-box Empirical Study
Through the comprehensive evaluations on five major cloud platforms: AWS, Azure, Google Cloud, Baidu Cloud, and Alibaba Cloud, we demonstrate that our image processing based attacks can reach a success rate of approximately 100%, and the semantic segmentation based attacks have a success rate over 90% among different detection services, such as violence, politician, and pornography detection.
