54 citations · 172 across the 15 of their papers we have counts for
17 papers
Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular Property Prediction
Minghao Guo, Veronika Thost, Samuel W Song +4
The prediction of molecular properties is a crucial task in the field of material and drug discovery. The potential benefits of using deep learning techniques are reflected in the…
Transformers over Directed Acyclic Graphs
Yuankai Luo, Veronika Thost, Lei Shi
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 gr…
Data-Efficient Graph Grammar Learning for Molecular Generation
Minghao Guo, Veronika Thost, Beichen Li +3
The problem of molecular generation has received significant attention recently. Existing methods are typically based on deep neural networks and require training on large datasets…
Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representation
Yufan Zhuang, Sahil Suneja, Veronika Thost +3
Identifying vulnerable code is a precautionary measure to counter software security breaches. Tedious expert effort has been spent to build static analyzers, yet insecure patterns…
Improving Inductive Link Prediction Using Hyper-Relational Facts
Mehdi Ali, Max Berrendorf, Mikhail Galkin +4
For many years, link prediction on knowledge graphs (KGs) has been a purely transductive task, not allowing for reasoning on unseen entities. Recently, increasing efforts are put i…
Relation Matters in Sampling: A Scalable Multi-Relational Graph Neural Network for Drug-Drug Interaction Prediction
Arthur Feeney, Rishabh Gupta, Veronika Thost +4
Sampling is an established technique to scale graph neural networks to large graphs. Current approaches however assume the graphs to be homogeneous in terms of relations and ignore…
CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks
Ruchir Puri, David S. Kung, Geert Janssen +14
Over the last several decades, software has been woven into the fabric of every aspect of our society. As software development surges and code infrastructure of enterprise applicat…
Directed Acyclic Graph Neural Networks
Veronika Thost, Jie Chen
Graph-structured data ubiquitously appears in science and engineering. Graph neural networks (GNNs) are designed to exploit the relational inductive bias exhibited in graphs; they…
Exploring Software Naturalness through Neural Language Models
Luca Buratti, Saurabh Pujar, Mihaela Bornea +8
The Software Naturalness hypothesis argues that programming languages can be understood through the same techniques used in natural language processing. We explore this hypothesis…
Temporal Conjunctive Query Answering in the Extended DL-Lite Family
Stefan Borgwardt, Veronika Thost
Ontology-based query answering (OBQA) augments classical query answering in databases by domain knowledge encoded in an ontology. Systems for OBQA use the ontological knowledge to…
An Experimental Study of Formula Embeddings for Automated Theorem Proving in First-Order Logic
Ibrahim Abdelaziz, Veronika Thost, Maxwell Crouse +1
Automated theorem proving in first-order logic is an active research area which is successfully supported by machine learning. While there have been various proposals for encoding…
Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks
Pavan Kapanipathi, Veronika Thost, Siva Sankalp Patel +10
Textual entailment is a fundamental task in natural language processing. Most approaches for solving the problem use only the textual content present in training data. A few approa…
Improving Graph Neural Network Representations of Logical Formulae with Subgraph Pooling
Maxwell Crouse, Ibrahim Abdelaziz, Cristina Cornelio +4
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. I…
A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving
Maxwell Crouse, Ibrahim Abdelaziz, Bassem Makni +7
Automated theorem provers have traditionally relied on manually tuned heuristics to guide how they perform proof search. Deep reinforcement learning has been proposed as a way to o…
RuDaS: Synthetic Datasets for Rule Learning and Evaluation Tools
Cristina Cornelio, Veronika Thost
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…
Metric Temporal Extensions of DL-Lite and Interval-Rigid Names
Veronika Thost
The DL-Lite description logics allow for modeling domain knowledge on top of databases and for efficient reasoning. We focus on metric temporal extensions of DL-Lite_bool and its f…
Query Answering for Rough EL Ontologies (Extended Technical Report)
Rafael Peñaloza, Veronika Thost, Anni-Yasmin Turhan
Querying large datasets with incomplete and vague data is still a challenge. Ontology-based query answering extends standard database query answering by background knowledge from a…