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Found 4 papers, 1 papers with code
MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting
Time series forecasting is crucial in many fields, yet current deep learning models struggle with noise, data sparsity, and capturing complex multi-scale patterns.
Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning
To address these challenges, we propose TLMG4Eth that combines a transaction language model with graph-based methods to capture semantic, similarity, and structural features of transaction data in Ethereum.
The Role of Transformer Models in Advancing Blockchain Technology: A Systematic Survey
As blockchain technology rapidly evolves, the demand for enhanced efficiency, security, and scalability grows. Transformer models, as powerful deep learning architectures, have shown unprecedented potential in addressing various blockchain challenges.
Struggle with Adversarial Defense? Try Diffusion
Unlike data-driven classifiers, TMDC, guided by Bayesian principles, utilizes the conditional likelihood from diffusion models to determine the class probabilities of input images, thereby insulating against the influences of data shift and the limitations of adversarial training.
