no code implementations • 24 Oct 2022 • Yuankai Luo, Veronika Thost, YiCheng Pan, 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 graph neural networks.
Ranked #2 on Graph Property Prediction on ogbg-code2
1 code implementation • ICLR 2022 • Minghao Guo, Veronika Thost, Beichen Li, Payel Das, Jie Chen, Wojciech Matusik
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.
no code implementations • 29 Sep 2021 • EunJeong Hwang, Veronika Thost, Shib Sankar Dasgupta, Tengfei Ma
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.
no code implementations • 7 Sep 2021 • Yufan Zhuang, Sahil Suneja, Veronika Thost, Giacomo Domeniconi, Alessandro Morari, Jim Laredo
Identifying vulnerable code is a precautionary measure to counter software security breaches.
1 code implementation • 10 Jul 2021 • Mehdi Ali, Max Berrendorf, Mikhail Galkin, Veronika Thost, Tengfei Ma, Volker Tresp, Jens Lehmann
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.
no code implementations • 28 May 2021 • Arthur Feeney, Rishabh Gupta, Veronika Thost, Rico Angell, Gayathri Chandu, Yash Adhikari, Tengfei Ma
Sampling is an established technique to scale graph neural networks to large graphs.
1 code implementation • 25 May 2021 • Ruchir Puri, David S. Kung, Geert Janssen, Wei zhang, Giacomo Domeniconi, Vladimir Zolotov, Julian Dolby, Jie Chen, Mihir Choudhury, Lindsey Decker, Veronika Thost, Luca Buratti, Saurabh Pujar, Shyam Ramji, Ulrich Finkler, Susan Malaika, Frederick Reiss
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.
1 code implementation • ICLR 2021 • Veronika Thost, Jie Chen
Graph-structured data ubiquitously appears in science and engineering.
Ranked #7 on Graph Property Prediction on ogbg-code2
no code implementations • 22 Jun 2020 • Luca Buratti, Saurabh Pujar, Mihaela Bornea, Scott McCarley, Yunhui Zheng, Gaetano Rossiello, Alessandro Morari, Jim Laredo, Veronika Thost, Yufan Zhuang, Giacomo Domeniconi
We explore this hypothesis through the use of a pre-trained transformer-based language model to perform code analysis tasks.
no code implementations • 2 Feb 2020 • Ibrahim Abdelaziz, Veronika Thost, Maxwell Crouse, Achille Fokoue
Automated theorem proving in first-order logic is an active research area which is successfully supported by machine learning.
1 code implementation • arXiv 2020 • Maxwell Crouse, Ibrahim Abdelaziz, Cristina Cornelio, Veronika Thost, Lingfei Wu, Kenneth Forbus, Achille Fokoue
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)
no code implementations • 5 Nov 2019 • Pavan Kapanipathi, Veronika Thost, Siva Sankalp Patel, Spencer Whitehead, Ibrahim Abdelaziz, Avinash Balakrishnan, Maria Chang, Kshitij Fadnis, Chulaka Gunasekara, Bassem Makni, Nicholas Mattei, Kartik Talamadupula, Achille Fokoue
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.
1 code implementation • 5 Nov 2019 • Maxwell Crouse, Ibrahim Abdelaziz, Bassem Makni, Spencer Whitehead, Cristina Cornelio, Pavan Kapanipathi, Kavitha Srinivas, Veronika Thost, Michael Witbrock, Achille Fokoue
Automated theorem provers have traditionally relied on manually tuned heuristics to guide how they perform proof search.
1 code implementation • 16 Sep 2019 • 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 compact form.
Ranked #1 on Inductive logic programming on RuDaS
Inductive knowledge graph completion Inductive logic programming +2