* **code2seq** (ICLR'2019) is our newer model. It uses LSTMs to encode paths node-by-node (rather than monolithic path embeddings as in code2vec), and an LSTM to decode a target sequence (rather than predicting a single label at a time as in code2vec). See [PDF](https://openreview.net/pdf?id=H1gKYo09tX), demo at [http://www.code2seq.org](http://www.code2seq.org) and [code](https://github.com/tech-srl/code2seq/).

* **Structural Language Models for Any-Code Generation** is a new paper that learns to generate the missing code within a larger code snippet. This is similar to code completion, but is able to predict complex expressions rather than a single token at a time. See [PDF](https://arxiv.org/pdf/1910.00577.pdf) (demo: soon).

* **Adversarial Examples for Models of Code** is a new paper that shows how to slightly mutate the input code snippet of code2vec and GNNs models (thus, introducing adversarial examples), such that the model (code2vec or GNNs) will output a prediction of our choice. See [PDF](https://arxiv.org/pdf/1910.07517.pdf) (code: soon).

* **Neural Reverse Engineering of Stripped Binaries** is a new paper that learns to predict procedure names in stripped binaries, thus use neural networks for reverse engineering. See [PDF](https://arxiv.org/pdf/1902.09122) (code: soon).

* TensorFlow - version 2.0.0 ([install](https://www.tensorflow.org/install/install_linux)).