LIGER blends symbolic and concrete traces to learn precise semantic program embeddings, outperforming syntax-based models on CoSET classification and code2seq on method name prediction while using fewer executions.
A neural probabilistic language model,
2 Pith papers cite this work. Polarity classification is still indexing.
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2019 2verdicts
UNVERDICTED 2representative citing papers
An informed machine learning approach using LSTM networks and expert-driven visual clustering to model normal behavior and detect misuse in system logs.
citing papers explorer
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Learning Blended, Precise Semantic Program Embeddings
LIGER blends symbolic and concrete traces to learn precise semantic program embeddings, outperforming syntax-based models on CoSET classification and code2seq on method name prediction while using fewer executions.
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System Misuse Detection via Informed Behavior Clustering and Modeling
An informed machine learning approach using LSTM networks and expert-driven visual clustering to model normal behavior and detect misuse in system logs.