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Found 10 papers, 4 papers with code
Population Graph Cross-Network Node Classification for Autism Detection Across Sample Groups
Graph neural networks (GNN) are a powerful tool for combining imaging and non-imaging medical information for node classification tasks.
DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data
Accurate forecasting of extreme values in time series is critical due to the significant impact of extreme events on human and natural systems.
COMET Flows: Towards Generative Modeling of Multivariate Extremes and Tail Dependence
In particular, existing normalizing flow architectures struggle to model multivariate extremes, characterized by heavy-tailed marginal distributions and asymmetric tail dependence among variables.
Defending Backdoor Data Poisoning Attacks by Using Noisy Label Defense Algorithm
We first discuss the limitations of directly using the noisy-label defense algorithms to defend against backdoor attacks.
Learning Deep Neural Networks under Agnostic Corrupted Supervision
Training deep neural models in the presence of corrupted supervision is challenging as the corrupted data points may significantly impact the generalization performance.
Unsupervised Anomaly Detection by Robust Collaborative Autoencoders
Unsupervised anomaly detection plays a crucial role in many critical applications.
Provable Robust Learning under Agnostic Corrupted Supervision
Training deep neural models in the presence of corrupted supervisions is challenging as the corrupted data points may significantly impact the generalization performance.
Fairness Perception from a Network-Centric Perspective
We show how the function can be extended to a group fairness metric known as fairness visibility and demonstrate its relationship to demographic parity.
Spatially Constrained Spectral Clustering Algorithms for Region Delineation
To address these two challenges, first, we develop a spatially constrained spectral clustering framework for region delineation that incorporates the tradeoff between region homogeneity and spatial contiguity.
A Low Rank Weighted Graph Convolutional Approach to Weather Prediction
Specifically, our proposed approach uses an LSTM to capture the inherent temporal autocorrelation of the data and a graph convolution to model its spatial relationships.
