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Code for the model presented in the paper: "code2seq: Generating Sequences from Structured Representations of Code"

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TensorFlow code for the neural network presented in the paper: "code2vec: Learning Distributed Representations of Code"

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Code for the paper "How Attentive are Graph Attention Networks?" (ICLR'2022)

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Code and resources for the paper: "Neural Reverse Engineering of Stripped Binaries using Augmented Control Flow Graphs"

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TensorFlow code for the neural network presented in the paper: "Structural Language Models of Code" (ICML'2020)

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Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"

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Code for the paper "A Structural Model for Contextual Code Changes"

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statistical similarity of binaries (Esh)

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implementation of ICML 2018 paper, Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples

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Demonstration of the path-extraction process shown in the paper "A General Path-Based Representation for Predicting Program Properties"

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