Analyzing differentiable fuzzy logic operators E van Krieken, E Acar, F van Harmelen Artificial Intelligence 302, 103602, 2022 | 60 | 2022 |
Analyzing Differentiable Fuzzy Implications E van Krieken, E Acar, F van Harmelen International Conference on Principles of Knowledge Representation and …, 2020 | 24 | 2020 |
Semi-Supervised Learning using Differentiable Reasoning E van Krieken, E Acar, F van Harmelen IFCoLog Journal of Logic and its Applications 6 (4), 633-653, 2019 | 21 | 2019 |
Storchastic: A framework for general stochastic automatic differentiation E van Krieken, J Tomczak, A Ten Teije Advances in Neural Information Processing Systems 34, 7574-7587, 2021 | 9 | 2021 |
Prompting as probing: Using language models for knowledge base construction D Alivanistos, SB Santamarķa, M Cochez, JC Kalo, E van Krieken, ... arXiv preprint arXiv:2208.11057, 2022 | 8 | 2022 |
Refining neural network predictions using background knowledge A Daniele, E van Krieken, L Serafini, F van Harmelen Machine Learning, 1-39, 2023 | 3 | 2023 |
A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference E van Krieken, T Thanapalasingam, JM Tomczak, F van Harmelen, ... arXiv preprint arXiv:2212.12393, 2022 | 2 | 2022 |
Benefits of social learning in physical robots J Heinerman, B Bussmann, R Groenendijk, E Van Krieken, J Slik, A Tezza, ... 2018 IEEE Symposium Series on Computational Intelligence (SSCI), 851-858, 2018 | 1 | 2018 |
Analysis of Measure-Valued Derivatives in a Reinforcement Learning Actor-Critic Framework K van den Houten, E van Krieken, B Heidergott 2022 Winter Simulation Conference (WSC), 2736-2747, 2022 | | 2022 |
Differentiable Fuzzy Logics E van Krieken Vrije Universiteit, 2019 | | 2019 |