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Found 6 papers, 2 papers with code
Knowledge Distillation on Spatial-Temporal Graph Convolutional Network for Traffic Prediction
Recognizing the significance of timely prediction due to the dynamic nature of real-time data, we employ knowledge distillation (KD) as a solution to enhance the execution time of ST-GNNs for traffic prediction.
Stateless and Rule-Based Verification For Compliance Checking Applications
Both of deductive and model checking verification techniques are relying on a notion of state and as a result, their underlying computational models are state dependent.
SELM: Software Engineering of Machine Learning Models
In this article, we present a SELM framework for Software Engineering of machine Learning Models.
Phishing Detection Using Machine Learning Techniques
One of the most successful methods for detecting these malicious activities is Machine Learning.
A Deep Learning-Based Approach for Measuring the Domain Similarity of Persian Texts
We generated a significant number of paired texts from this dataset and assigned each pair a score from 0 to 3, which demonstrates the degree of similarity between the domains of the pair.
DAST Model: Deciding About Semantic Complexity of a Text
DAST proposes an intuitionistic approach to semantics that lets us have a well-defined model for the semantics of a text and its complexity: semantic is considered as a lattice of intuitions and, as a result, semantic complexity is defined as the result of a calculation on this lattice.
