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05/04/2023
On the Expressivity Role of LayerNorm in Transformers' Attention
Layer Normalization (LayerNorm) is an inherent component in all Transfor...
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03/01/2023
Diffusing Graph Attention
The dominant paradigm for machine learning on graphs uses Message Passin...
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06/13/2021
Thinking Like Transformers
What is the computational model behind a Transformer? Where recurrent ne...
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05/30/2021
How Attentive are Graph Attention Networks?
Graph Attention Networks (GATs) are one of the most popular GNN architec...
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06/09/2020
On the Bottleneck of Graph Neural Networks and its Practical Implications
Graph neural networks (GNNs) were shown to effectively learn from highly...
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05/27/2020
A Structural Model for Contextual Code Changes
We address the problem of predicting edit completions based on a learned...
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04/18/2020
A Formal Hierarchy of RNN Architectures
We develop a formal hierarchy of the expressive capacity of RNN architec...
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10/30/2019
Learning Deterministic Weighted Automata with Queries and Counterexamples
We present an algorithm for extraction of a probabilistic deterministic ...
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10/15/2019
Adversarial Examples for Models of Code
We introduce a novel approach for attacking trained models of code with ...
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09/30/2019
Structural Language Models of Code
We address the problem of any-code completion - generating a missing pie...
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09/30/2019
Structural Language Models for Any-Code Generation
We address the problem of Any-Code Generation (AnyGen) - generating code...
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05/20/2019
Towards Neural Decompilation
We address the problem of automatic decompilation, converting a program ...
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02/25/2019
Neural Reverse Engineering of Stripped Binaries
We address the problem of predicting procedure names in stripped executa...
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08/04/2018
code2seq: Generating Sequences from Structured Representations of Code
The ability to generate natural language sequences from source code snip...
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05/13/2018
On the Practical Computational Power of Finite Precision RNNs for Language Recognition
While Recurrent Neural Networks (RNNs) are famously known to be Turing c...
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03/26/2018
A General Path-Based Representation for Predicting Program Properties
Predicting program properties such as names or expression types has a wi...
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03/26/2018
code2vec: Learning Distributed Representations of Code
We present a neural model for representing snippets of code as continuou...
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10/03/2017
