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Found 23 papers, 12 papers with code
Air Quality Prediction with Physics-Informed Dual Neural ODEs in Open Systems
To address these challenges, we propose Air-DualODE, a novel physics-informed approach that integrates dual branches of Neural ODEs for air quality prediction.
SEFraud: Graph-based Self-Explainable Fraud Detection via Interpretative Mask Learning
While considerable efforts have been devoted to designing adequate fraud detectors, the interpretability of their results has often been overlooked.
Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders
The significant divergence of time series data across different domains presents two primary challenges in building such a general model: (1) meeting the diverse requirements of appropriate information bottlenecks tailored to different datasets in one unified model, and (2) enabling distinguishment between multiple normal and abnormal patterns, both are crucial for effective anomaly detection in various target scenarios.
ROSE: Register Assisted General Time Series Forecasting with Decomposed Frequency Learning
Enabling general time series forecasting faces two challenges: how to obtain unified representations from multi-domian time series data, and how to capture domain-specific features from time series data across various domains for adaptive transfer in downstream tasks.
Ranked #10 on
Time Series Forecasting
on ETTh1 (336) Multivariate
Enhancing Multivariate Time Series Forecasting with Mutual Information-driven Cross-Variable and Temporal Modeling
To substantiate this claim, we introduce the Cross-variable Decorrelation Aware feature Modeling (CDAM) for Channel-mixing approaches, aiming to refine Channel-mixing by minimizing redundant information between channels while enhancing relevant mutual information.
MATA*: Combining Learnable Node Matching with A* Algorithm for Approximate Graph Edit Distance Computation
Graph Edit Distance (GED) is a general and domain-agnostic metric to measure graph similarity, widely used in graph search or retrieving tasks.
Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning
Graph Active Learning (GAL), which aims to find the most informative nodes in graphs for annotation to maximize the Graph Neural Networks (GNNs) performance, has attracted many research efforts but remains non-trivial challenges.
A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning
Recent studies have shown great promise in unsupervised representation learning (URL) for multivariate time series, because URL has the capability in learning generalizable representation for many downstream tasks without using inaccessible labels.
Inducing Neural Collapse in Deep Long-tailed Learning
One of the reasons is that the learned representations (i. e. features) from the imbalanced datasets are less effective than those from balanced datasets.
Generative Oversampling for Imbalanced Data via Majority-Guided VAE
Learning with imbalanced data is a challenging problem in deep learning.
MTS-Mixers: Multivariate Time Series Forecasting via Factorized Temporal and Channel Mixing
Specifically, we find that (1) attention is not necessary for capturing temporal dependencies, (2) the entanglement and redundancy in the capture of temporal and channel interaction affect the forecasting performance, and (3) it is important to model the mapping between the input and the prediction sequence.
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
kHGCN: Tree-likeness Modeling via Continuous and Discrete Curvature Learning
The prevalence of tree-like structures, encompassing hierarchical structures and power law distributions, exists extensively in real-world applications, including recommendation systems, ecosystems, financial networks, social networks, etc.
Hyperbolic Graph Representation Learning: A Tutorial
We first give a brief introduction to graph representation learning as well as some preliminary Riemannian and hyperbolic geometry.
Discovering Representative Attribute-stars via Minimum Description Length
However, there are generally two limitations that hinder their practical use: (1) they have multiple parameters that are hard to set but greatly influence results, (2) and they generally focus on identifying complex subgraphs while ignoring relationships between attributes of nodes. Graphs are a popular data type found in many domains.
TeleGraph: A Benchmark Dataset for Hierarchical Link Prediction
Link prediction is a key problem for network-structured data, attracting considerable research efforts owing to its diverse applications.
Hyperbolic Graph Neural Networks: A Review of Methods and Applications
Graph neural networks generalize conventional neural networks to graph-structured data and have received widespread attention due to their impressive representation ability.
gCastle: A Python Toolbox for Causal Discovery
$\texttt{gCastle}$ is an end-to-end Python toolbox for causal structure learning.
Label-Aware Distribution Calibration for Long-tailed Classification
Real-world data usually present long-tailed distributions.
An Ensemble Noise-Robust K-fold Cross-Validation Selection Method for Noisy Labels
E-NKCVS is empirically shown to be highly tolerant to considerable proportions of label noise and has a consistent improvement over state-of-the-art methods.
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.
Spatio-Temporal Hybrid Graph Convolutional Network for Traffic Forecasting in Telecommunication Networks
We conduct extensive experiments on real-world traffic datasets collected from telecommunication networks.
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.
