| A semantic loss function for deep learning with symbolic knowledge J Xu, Z Zhang, T Friedman, Y Liang, G Van den Broeck International Conference on Machine Learning, 5502-5511, 2018 | 596 | 2018 |
| PhenomeCentral: a portal for phenotypic and genotypic matchmaking of patients with rare genetic diseases OJ Buske, M Girdea, S Dumitriu, B Gallinger, T Hartley, H Trang, ... Human mutation 36 (10), 931-940, 2015 | 142 | 2015 |
| Semantic and generalized entropy loss functions for semi-supervised deep learning K Gajowniczek, Y Liang, T Friedman, T Ząbkowski, G Van den Broeck Entropy 22 (3), 334, 2020 | 28 | 2020 |
| Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings T Friedman, G Van den Broeck arXiv preprint arXiv:2002.10029, 2020 | 26 | 2020 |
| Approximate knowledge compilation by online collapsed importance sampling T Friedman, G Van den Broeck Advances in Neural Information Processing Systems, 8024-8034, 2018 | 24 | 2018 |
| Solving Marginal MAP Exactly by Probabilistic Circuit Transformations YJ Choi, T Friedman, G Van den Broeck International Conference on Artificial Intelligence and Statistics, 10196-10208, 2022 | 16 | 2022 |
| On Constrained Open-World Probabilistic Databases T Friedman, GV Broeck arXiv preprint arXiv:1902.10677, 2019 | 14 | 2019 |
| Scalable Rule Learning in Probabilistic Knowledge Bases A Jain, T Friedman, O Kuzelka, G Van den Broeck, L De Raedt Automated Knowledge Base Construction, 2019 | 11 | 2019 |
| Insights from the Intersection of Logic and Probabilistic Reasoning T Friedman University of California, Los Angeles, 2021 | | 2021 |
| Approximate Knowledge Compilation by Online Collapsed Importance Sampling T Friedman, G Van den Broeck Advances in Neural Information Processing Systems 31, 2018 | | 2018 |
Guy Van den BroeckProfessor and Samueli Fellow, UCLAVerified email at cs.ucla.edu
