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ORCIDEmile van Krieken
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- affiliation: University of Edinburgh, UK
- affiliation: Vrije Universiteit Amsterdam, The Netherlands
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2020 – today
- 2024
[c8]Emile van Krieken, Pasquale Minervini, Edoardo M. Ponti, Antonio Vergari:
On the Independence Assumption in Neurosymbolic Learning. ICML 2024- [c7]Emile van Krieken
, Samy Badreddine, Robin Manhaeve, Eleonora Giunchiglia:
ULLER: A Unified Language for Learning and Reasoning. NeSy (1) 2024: 219-239 - [i15]Emile van Krieken:
Optimisation in Neurosymbolic Learning Systems. CoRR abs/2401.10819 (2024) - [i14]Emanuele Marconato, Samuele Bortolotti, Emile van Krieken, Antonio Vergari, Andrea Passerini, Stefano Teso:
BEARS Make Neuro-Symbolic Models Aware of their Reasoning Shortcuts. CoRR abs/2402.12240 (2024) - [i13]Emile van Krieken, Pasquale Minervini, Edoardo M. Ponti, Antonio Vergari:
On the Independence Assumption in Neurosymbolic Learning. CoRR abs/2404.08458 (2024) - [i12]Emile van Krieken, Samy Badreddine, Robin Manhaeve, Eleonora Giunchiglia:
ULLER: A Unified Language for Learning and Reasoning. CoRR abs/2405.00532 (2024) - [i11]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 abs/2406.04127 (2024) - [i10]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 abs/2406.10368 (2024) - 2023
- [j3]Alessandro Daniele, Emile van Krieken, Luciano Serafini, Frank van Harmelen:
Refining neural network predictions using background knowledge. Mach. Learn. 112(9): 3293-3331 (2023) - [c6]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 - [d2]Thiviyan Thanapalasingam, Emile van Krieken, Peter Bloem, Paul Groth:
IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation. Zenodo, 2023
- [d1]Thiviyan Thanapalasingam, Emile van Krieken, Peter Bloem, Paul Groth:
IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation. Zenodo, 2023 - [i9]Thiviyan Thanapalasingam, Emile van Krieken, Peter Bloem, Paul Groth:
IntelliGraphs: Datasets for Benchmarking Knowledge Graph Generation. CoRR abs/2307.06698 (2023) - [i8]Taraneh Younesian, Thiviyan Thanapalasingam, Emile van Krieken, Daniel Daza, Peter Bloem:
GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks. CoRR abs/2310.03399 (2023) - 2022
- [j2]Emile van Krieken, Erman Acar, Frank van Harmelen:
Analyzing Differentiable Fuzzy Logic Operators. Artif. Intell. 302: 103602 (2022) - [c5]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: 11-34 - [c4]Kim van den Houten, Emile van Krieken, Bernd Heidergott:
Analysis of Measure-Valued Derivatives in a Reinforcement Learning Actor-Critic Framework. WSC 2022: 2736-2747 - [i7]Alessandro Daniele, Emile van Krieken, Luciano Serafini, Frank van Harmelen:
Refining neural network predictions using background knowledge. CoRR abs/2206.04976 (2022) - [i6]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 abs/2208.11057 (2022) - [i5]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 abs/2212.12393 (2022) - 2021
- [c3]Emile van Krieken, Jakub M. Tomczak, Annette ten Teije:
Storchastic: A Framework for General Stochastic Automatic Differentiation. NeurIPS 2021: 7574-7587 - [i4]Emile van Krieken, Jakub M. Tomczak, Annette ten Teije:
Storchastic: A Framework for General Stochastic Automatic Differentiation. CoRR abs/2104.00428 (2021) - 2020
- [c2]Emile van Krieken, Erman Acar, Frank van Harmelen:
Analyzing Differentiable Fuzzy Implications. KR 2020: 893-903 - [i3]Emile van Krieken, Erman Acar, Frank van Harmelen:
Analyzing Differentiable Fuzzy Logic Operators. CoRR abs/2002.06100 (2020) - [i2]Emile van Krieken, Erman Acar, Frank van Harmelen:
Analyzing Differentiable Fuzzy Implications. CoRR abs/2006.03472 (2020)
2010 – 2019
- 2019
- [j1]Emile van Krieken, Erman Acar, Frank van Harmelen:
Semi-supervised Learning using Differentiable Reasoning. FLAP 6(4): 633-652 (2019) - [i1]Emile van Krieken, Erman Acar, Frank van Harmelen:
Semi-Supervised Learning using Differentiable Reasoning. CoRR abs/1908.04700 (2019) - 2018
- [c1]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: 851-858
Coauthor Index
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last updated on 2024-11-08 20:28 CET by the dblp team
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