Search Results for author:
Found 10 papers, 7 papers with code
DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain Adaptation
To address the issue, we study the problem of active graph domain adaptation, which selects a small quantitative of informative nodes on the target graph for extra annotation.
A Comprehensive Graph Pooling Benchmark: Effectiveness, Robustness and Generalizability
Graph pooling has gained attention for its ability to obtain effective node and graph representations for various downstream tasks.
OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection
Out-of-Distribution (OOD) detection is critical for the reliable operation of open-world intelligent systems.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Generative Oversampling for Imbalanced Data via Majority-Guided VAE
Learning with imbalanced data is a challenging problem in deep learning.
Ti-MAE: Self-Supervised Masked Time Series Autoencoders
Multivariate Time Series forecasting has been an increasingly popular topic in various applications and scenarios.
Contrastive Learning
Multivariate Time Series Forecasting
+2
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
Out-of-distribution (OOD) detection is vital to safety-critical machine learning applications and has thus been extensively studied, with a plethora of methods developed in the literature.
Multi-relation Message Passing for Multi-label Text Classification
These examples motivate the modelling of multiple types of bi-directional relationships between labels.
Multi-Label Classification
Multi-Label Image Classification
+4
Label-Aware Distribution Calibration for Long-tailed Classification
Real-world data usually present long-tailed distributions.
Mask-GVAE: Blind Denoising Graphs via Partition
We present Mask-GVAE, a variational generative model for blind denoising large discrete graphs, in which "blind denoising" means we don't require any supervision from clean graphs.
Predicting Path Failure In Time-Evolving Graphs
Through experiments on a real-world telecommunication network and a traffic network in California, we demonstrate the superiority of LRGCN to other competing methods in path failure prediction, and prove the effectiveness of SAPE on path representation.
