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Found 3 papers, 1 papers with code
PST: Improving Quantitative Trading via Program Sketch-based Tuning
Particularly, PST first proposes using a novel symbolic program sketch to embed the abstract human expert knowledge of market trends.
Unveiling Project-Specific Bias in Neural Code Models
We propose a Cond-Idf measurement to interpret this behavior, which quantifies the relatedness of a token with a label and its project-specificness.
Why an Android App is Classified as Malware? Towards Malware Classification Interpretation
In this paper, to fill this gap, we propose a novel and interpretable ML-based approach (named XMal) to classify malware with high accuracy and explain the classification result meanwhile.
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