Gustav Sír

According to our database, Gustav Sír authored at least 39 papers between 2013 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Neural-Symbolic Knowledge Tracing: Injecting Educational Knowledge into Deep Learning for Responsible Learner Modelling.

Danial Hooshyar, Gustav Sír, Yeongwook Yang, Tommi Kärkkäinen, Raija Hämäläinen, Ekaterina Krivich, Mutlu Cukurova, Dragan Gasevic, Roger Azevedo

CoRR, April, 2026

2025

Geometric Deep Learning for the Rubik's Cube Group.

Martin Krutsky, Gustav Sír

IEEE Trans. Neural Networks Learn. Syst., December, 2025

Problems With Large Language Models for Learner Modelling: Why LLMs Alone Fall Short for Responsible Tutoring in K-12 Education.

Danial Hooshyar, Yeongwook Yang, Gustav Sír, Tommi Kärkkäinen, Raija Hämäläinen, Mutlu Cukurova, Roger Azevedo

CoRR, December, 2025

Tabular Transformers Meet Relational Databases.

Jakub Peleska, Gustav Sír

ACM Trans. Intell. Syst. Technol., October, 2025

Mission-Aligned Learning-Informed Control of Autonomous Systems: Formulation and Foundations.

Vyacheslav Kungurtsev, Gustav Sír, Akhil S. Anand, Sebastien Gros, Haozhe Tian, Homayoun Hamedmoghadam

CoRR, July, 2025

Towards Responsible and Trustworthy Educational Data Mining: Comparing Symbolic, Sub-Symbolic, and Neural-Symbolic AI Methods.

Danial Hooshyar, Eve Kikas, Yeongwook Yang, Gustav Sír, Raija Hämäläinen, Tommi Kärkkäinen, Roger Azevedo

CoRR, April, 2025

"Cause" is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of "Causal Machine Learning".

Vyacheslav Kungurtsev, Leonardo Christov Moore, Gustav Sír, Martin Krutsky

CoRR, January, 2025

Towards responsible AI for education: Hybrid human-AI to confront the elephant in the room.

Danial Hooshyar, Gustav Sír, Yeongwook Yang, Eve Kikas, Raija Hämäläinen, Tommi Kärkkäinen, Dragan Gasevic, Roger Azevedo

Comput. Educ. Artif. Intell., 2025

XAI Desiderata for Trustworthy AI: Insights from the AI Act.

Martin Krutsky, Jiri Nemecek, Jakub Peleska, Paula Gürtler, Gustav Sír

Proceedings of TRUST-AI 2025, 2025

Task-Agnostic Contrastive Pretraining for Relational Deep Learning.

Jakub Peleska, Gustav Sír

Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2025

ReDeLEx: A Framework for Relational Deep Learning Exploration.

Jakub Peleska, Gustav Sír

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2025

Binarizing Physics-Inspired GNNs for Combinatorial Optimization.

Martin Krutsky, Gustav Sír, Vyacheslav Kungurtsev, Georgios Korpas

Proceedings of the ECAI 2025 - 28th European Conference on Artificial Intelligence, 25-30 October 2025, Bologna, Italy, 2025

State Encodings for GNN-Based Lifted Planners.

Rostislav Horcík, Gustav Sír, Vítezslav Simek, Tomás Pevný

Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence, 2025

2024

Transformers Meet Relational Databases.

Jakub Peleska, Gustav Sír

CoRR, 2024

Deep Learning for Generalised Planning with Background Knowledge.

Dillon Z. Chen, Rostislav Horcík, Gustav Sír

CoRR, 2024

Expressiveness of Graph Neural Networks in Planning Domains.

Rostislav Horcík, Gustav Sír

Proceedings of the Thirty-Fourth International Conference on Automated Planning and Scheduling, 2024

2023

Lifted Relational Neural Networks: From Graphs to Deep Relational Learning.

Gustav Sír, Filip Zelezný, Ondrej Kuzelka

Proceedings of the Compendium of Neurosymbolic Artificial Intelligence, 2023

2022

Deep Learning with Relational Logic Representations

Gustav Sír

Frontiers in Artificial Intelligence and Applications 357, IOS Press, ISBN: 978-1-64368-342-3, 2022

2021

Beyond graph neural networks with lifted relational neural networks.

Gustav Sourek, Filip Zelezný, Ondrej Kuzelka

Mach. Learn., 2021

Optimal sports betting strategies in practice: an experimental review.

Matej Uhrín, Gustav Sourek, Ondrej Hubácek, Filip Zelezný

CoRR, 2021

Lossless Compression of Structured Convolutional Models via Lifting.

Gustav Sourek, Filip Zelezný, Ondrej Kuzelka

Proceedings of the 9th International Conference on Learning Representations, 2021

2020

Learning with Molecules beyond Graph Neural Networks.

Gustav Sourek, Filip Zelezný, Ondrej Kuzelka

CoRR, 2020

Beating the market with a bad predictive model.

Ondrej Hubácek, Gustav Sír

CoRR, 2020

Lossless Compression of Structured Convolutional Models via Lifting.

Gustav Sourek, Filip Zelezný

CoRR, 2020

2019

Efficient Extraction of Network Event Types from NetFlows.

Gustav Sourek, Filip Zelezný

Secur. Commun. Networks, 2019

Learning to predict soccer results from relational data with gradient boosted trees.

Ondrej Hubácek, Gustav Sourek, Filip Zelezný

Mach. Learn., 2019

Revisiting Neural-Symbolic Learning Cycle.

Martin Svatos, Gustav Sourek, Filip Zelezný

Proceedings of the 2019 International Workshop on Neural-Symbolic Learning and Reasoning (NeSy 2019), 2019

Scaling up relational templated neural models.

Gustav Sourek, Filip Zelezný, Ondrej Kuzelka

Proceedings of the 2019 International Workshop on Neural-Symbolic Learning and Reasoning (NeSy 2019), 2019

Deep Learning with Relational Logic Representations.

Gustav Sourek

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

2018

Lifted Relational Neural Networks: Efficient Learning of Latent Relational Structures.

Gustav Sourek, Vojtech Aschenbrenner, Filip Zelezný, Steven Schockaert, Ondrej Kuzelka

J. Artif. Intell. Res., 2018

Deep Learning from Spatial Relations for Soccer Pass Prediction.

Ondrej Hubácek, Gustav Sourek, Filip Zelezný

Proceedings of the 5th Workshop on Machine Learning and Data Mining for Sports Analytics co-located with 2018 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2018), 2018

Lifted Relational Team Embeddings for Predictive Sports Analytics.

Ondrej Hubácek, Gustav Sourek, Filip Zelezný

Proceedings of the Up-and-Coming and Short Papers of the 28th International Conference on Inductive Logic Programming (ILP 2018), 2018

2017

Pruning Hypothesis Spaces Using Learned Domain Theories.

Martin Svatos, Gustav Sourek, Filip Zelezný, Steven Schockaert, Ondrej Kuzelka

Proceedings of the Inductive Logic Programming - 27th International Conference, 2017

Stacked Structure Learning for Lifted Relational Neural Networks.

Gustav Sourek, Martin Svatos, Filip Zelezný, Steven Schockaert, Ondrej Kuzelka

Proceedings of the Inductive Logic Programming - 27th International Conference, 2017

2016

Learning Predictive Categories Using Lifted Relational Neural Networks.

Gustav Sourek, Suresh Manandhar, Filip Zelezný, Steven Schockaert, Ondrej Kuzelka

Proceedings of the Inductive Logic Programming - 26th International Conference, 2016

2015

Visual data-flow framework of evolutionary computation.

Gustav Sourek, Petr Posik

Proceedings of the 2015 Conference on research in adaptive and convergent systems, 2015

Lifted Relational Neural Networks.

Gustav Sourek, Vojtech Aschenbrenner, Filip Zelezný, Ondrej Kuzelka

Proceedings of the NIPS Workshop on Cognitive Computation: Integrating Neural and Symbolic Approaches co-located with the 29th Annual Conference on Neural Information Processing Systems (NIPS 2015), 2015

Learning to Detect Network Intrusion from a Few Labeled Events and Background Traffic.

Gustav Sourek, Ondrej Kuzelka, Filip Zelezný

Proceedings of the Intelligent Mechanisms for Network Configuration and Security, 2015

2013

Predicting Top-k Trends on Twitter using Graphlets and Time Features.

Gustav Sourek, Ondrej Kuzelka, Filip Zelezný

Proceedings of the Late Breaking Papers of the 23rd International Conference on Inductive Logic Programming, Rio de Janeiro, Brazil, August 28th - to, 2013