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Found 15 papers, 12 papers with code
A Systematic Evaluation of Large Language Models of Code
We aim to fill in some of these blanks through a systematic evaluation of the largest existing models: Codex, GPT-J, GPT-Neo, GPT-NeoX-20B, and CodeParrot, across various programming languages.
Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval
Retrieval-based language models (R-LM) model the probability of natural language text by combining a standard language model (LM) with examples retrieved from an external datastore at test time.
How Attentive are Graph Attention Networks?
Because GATs use a static attention mechanism, there are simple graph problems that GAT cannot express: in a controlled problem, we show that static attention hinders GAT from even fitting the training data.
Single-Node Attack for Fooling Graph Neural Networks
When the adversary is allowed to pick a specific attacker node, the attack is even more effective.
On the Bottleneck of Graph Neural Networks and its Practical Implications
Since the proposal of the graph neural network (GNN) by Gori et al. (2005) and Scarselli et al. (2008), one of the major problems in training GNNs was their struggle to propagate information between distant nodes in the graph.
A Structural Model for Contextual Code Changes
We conduct a thorough evaluation, comparing our approach to a variety of representation and modeling approaches that are driven by multiple strong models such as LSTMs, Transformers, and neural CRFs.
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Adversarial Examples for Models of Code
Our evaluations demonstrate that DAMP has up to 89% success rate in changing a prediction to the adversary's choice (a targeted attack) and a success rate of up to 94% in changing a given prediction to any incorrect prediction (a non-targeted attack).
Structural Language Models of Code
We introduce a new approach to any-code completion that leverages the strict syntax of programming languages to model a code snippet as a tree - structural language modeling (SLM).
Structural Language Models for Any-Code Generation
We introduce a new approach to AnyGen that leverages the strict syntax of programming languages to model a code snippet as tree structural language modeling (SLM).
Neural Reverse Engineering of Stripped Binaries using Augmented Control Flow Graphs
This is a challenging problem because of the low amount of syntactic information available in stripped executables, and the diverse assembly code patterns arising from compiler optimizations.
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.
Contextual Speech Recognition with Difficult Negative Training Examples
Improving the representation of contextual information is key to unlocking the potential of end-to-end (E2E) automatic speech recognition (ASR).
code2seq: Generating Sequences from Structured Representations of Code
The ability to generate natural language sequences from source code snippets has a variety of applications such as code summarization, documentation, and retrieval.
A General Path-Based Representation for Predicting Program Properties
A major challenge when learning from programs is $\textit{how to represent programs in a way that facilitates effective learning}$.
code2vec: Learning Distributed Representations of Code
We demonstrate the effectiveness of our approach by using it to predict a method's name from the vector representation of its body.
