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Found 14 papers, 7 papers with code
Transformers over Directed Acyclic Graphs
Transformer models have recently gained popularity in graph representation learning as they have the potential to learn complex relationships beyond the ones captured by regular graph neural networks.
Ranked #2 on
Graph Property Prediction
on ogbg-code2
Data-Efficient Graph Grammar Learning for Molecular Generation
This is a non-trivial task for neural network-based generative models since the relevant chemical knowledge can only be extracted and generalized from the limited training data.
Revisiting Virtual Nodes in Graph Neural Networks for Link Prediction
It is well known that the graph classification performance of graph neural networks often improves by adding an artificial virtual node to the graphs, which is connected to all nodes in the graph.
Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representation
Identifying vulnerable code is a precautionary measure to counter software security breaches.
Improving Inductive Link Prediction Using Hyper-Relational Facts
In this work, we classify different inductive settings and study the benefits of employing hyper-relational KGs on a wide range of semi- and fully inductive link prediction tasks powered by recent advancements in graph neural networks.
Relation Matters in Sampling: A Scalable Multi-Relational Graph Neural Network for Drug-Drug Interaction Prediction
Sampling is an established technique to scale graph neural networks to large graphs.
CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks
In addition to its large scale, CodeNet has a rich set of high-quality annotations to benchmark and help accelerate research in AI techniques for a variety of critical coding tasks, including code similarity and classification, code translation between a large variety of programming languages, and code performance (runtime and memory) improvement techniques.
Directed Acyclic Graph Neural Networks
Graph-structured data ubiquitously appears in science and engineering.
Ranked #7 on
Graph Property Prediction
on ogbg-code2
Exploring Software Naturalness through Neural Language Models
We explore this hypothesis through the use of a pre-trained transformer-based language model to perform code analysis tasks.
An Experimental Study of Formula Embeddings for Automated Theorem Proving in First-Order Logic
Automated theorem proving in first-order logic is an active research area which is successfully supported by machine learning.
Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling
Recent advances in the integration of deep learning with automated theorem proving have centered around the representation of logical formulae as inputs to deep learning systems.
Ranked #1 on
Automated Theorem Proving
on HolStep (Conditional)
Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks
A few approaches have shown that information from external knowledge sources like knowledge graphs (KGs) can add value, in addition to the textual content, by providing background knowledge that may be critical for a task.
A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving
Automated theorem provers have traditionally relied on manually tuned heuristics to guide how they perform proof search.
RuDaS: Synthetic Datasets for Rule Learning and Evaluation Tools
Logical rules are a popular knowledge representation language in many domains, representing background knowledge and encoding information that can be derived from given facts in a compact form.
Ranked #1 on
Inductive logic programming
on RuDaS
Inductive knowledge graph completion
Inductive logic programming
+2
