Emile van Krieken

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 24

Top Topics

Benchmark Suite

Independence Assumption

Top Venues

Publications

Emile van Krieken, Pasquale Minervini, Edoardo M. Ponti, Antonio Vergari
On the Independence Assumption in Neurosymbolic Learning. CoRR (2024)
Samuele Bortolotti, Emanuele Marconato, Tommaso Carraro, Paolo Morettin, Emile van Krieken, Antonio Vergari, Stefano Teso, Andrea Passerini
A Benchmark Suite for Systematically Evaluating Reasoning Shortcuts. CoRR (2024)
Emile van Krieken, Samy Badreddine, Robin Manhaeve, Eleonora Giunchiglia
ULLER: A Unified Language for Learning and Reasoning. CoRR (2024)
Emile van Krieken
Optimisation in Neurosymbolic Learning Systems. CoRR (2024)
Emanuele Marconato, Samuele Bortolotti, Emile van Krieken, Antonio Vergari, Andrea Passerini, Stefano Teso
BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts. CoRR (2024)
Aryo Pradipta Gema, Joshua Ong Jun Leang, Giwon Hong, Alessio Devoto, Alberto Carlo Maria Mancino, Rohit Saxena, Xuanli He, Yu Zhao, Xiaotang Du, Mohammad Reza Ghasemi Madani, Claire Barale, Robert McHardy, Joshua Harris, Jean Kaddour, Emile van Krieken, Pasquale Minervini
Are We Done with MMLU? CoRR (2024)
Thiviyan Thanapalasingam, Emile van Krieken, Peter Bloem, Paul Groth
IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation. CoRR (2023)
Taraneh Younesian, Thiviyan Thanapalasingam, Emile van Krieken, Daniel Daza, Peter Bloem
GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks. CoRR (2023)
Emile van Krieken, Thiviyan Thanapalasingam, Jakub M. Tomczak, Frank van Harmelen, Annette ten Teije
A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference. NeurIPS (2023)
Alessandro Daniele, Emile van Krieken, Luciano Serafini, Frank van Harmelen
Refining neural network predictions using background knowledge. Mach. Learn. 112 (9) (2023)
Emile van Krieken, Thiviyan Thanapalasingam, Jakub M. Tomczak, Frank van Harmelen, Annette ten Teije
A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference. CoRR (2022)
Alessandro Daniele, Emile van Krieken, Luciano Serafini, Frank van Harmelen
Refining neural network predictions using background knowledge. CoRR (2022)
Dimitrios Alivanistos, Selene Baez Santamaría, Michael Cochez, Jan-Christoph Kalo, Emile van Krieken, Thiviyan Thanapalasingam
Prompting as Probing: Using Language Models for Knowledge Base Construction. LM-KBC@ISWC (2022)
Kim van den Houten, Emile van Krieken, Bernd Heidergott
Analysis of Measure-Valued Derivatives in a Reinforcement Learning Actor-Critic Framework. WSC (2022)
Emile van Krieken, Erman Acar, Frank van Harmelen
Analyzing Differentiable Fuzzy Logic Operators. Artif. Intell. 302 (2022)
Dimitrios Alivanistos, Selene Baez Santamaría, Michael Cochez, Jan-Christoph Kalo, Emile van Krieken, Thiviyan Thanapalasingam
Prompting as Probing: Using Language Models for Knowledge Base Construction. CoRR (2022)
Emile van Krieken, Jakub M. Tomczak, Annette ten Teije
Storchastic: A Framework for General Stochastic Automatic Differentiation. NeurIPS (2021)
Emile van Krieken, Jakub M. Tomczak, Annette ten Teije
Storchastic: A Framework for General Stochastic Automatic Differentiation. CoRR (2021)
Emile van Krieken, Erman Acar, Frank van Harmelen
Analyzing Differentiable Fuzzy Implications. KR (2020)
Emile van Krieken, Erman Acar, Frank van Harmelen
Analyzing Differentiable Fuzzy Implications. CoRR (2020)
Emile van Krieken, Erman Acar, Frank van Harmelen
Analyzing Differentiable Fuzzy Logic Operators. CoRR (2020)
Emile van Krieken, Erman Acar, Frank van Harmelen
Semi-supervised Learning using Differentiable Reasoning. FLAP 6 (4) (2019)
Emile van Krieken, Erman Acar, Frank van Harmelen
Semi-Supervised Learning using Differentiable Reasoning. CoRR (2019)
Jacqueline Heinerman, Bart Bussmann, Rick Groenendijk, Emile van Krieken, Jesper Slik, Alessandro Tezza, Evert Haasdijk, A. E. Eiben
Benefits of Social Learning in Physical Robots. SSCI (2018)