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Found 5 papers, 4 papers with code
Simplified Mamba with Disentangled Dependency Encoding for Long-Term Time Series Forecasting
Along this line, we propose a disentangled dependency encoding strategy to endow Mamba with efficient cross-variate dependency modeling capability while minimizing the interference between time and variate dimensions.
Interpretable Cascading Mixture-of-Experts for Urban Traffic Congestion Prediction
Rapid urbanization has significantly escalated traffic congestion, underscoring the need for advanced congestion prediction services to bolster intelligent transportation systems.
When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook
Specifically, we first employ causal tools to analyze the primary trustworthiness risks of existing GNNs, underscoring the necessity for GNNs to comprehend the causal mechanisms within graph data.
Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models
Remarkably, we also show that adversarial training with our proposed attacks can significantly improve the robustness of spatiotemporal traffic forecasting models.
On audio enhancement via online non-negative matrix factorization
We propose a method for noise reduction, the task of producing a clean audio signal from a recording corrupted by additive noise.
