Asma, Z., Hernández, D., Galárraga, L., Flouris, G., Fundulaki, I., & Hose, K. (2024, Mai). NPCS: Native Provenance Computation for SPARQL.
Proceedings of the ACM Web Conference 2024 (WWW ’24), May13--17, 2024, Singapore, Singapore. WWW ’24, Singapore.
https://doi.org/10.1145/3589334.3645557
BibTeX
Asma, Z., Hernández, D., Galárraga, L., Flouris, G., Fundulaki, I., & Hose, K. (2024, Mai). NPCS: Native Provenance Computation for SPARQL.
Proceedings of the ACM Web Conference 2024 (WWW ’24), May13--17, 2024, Singapore, Singapore. WWW ’24, Singapore.
https://doi.org/10.1145/3589334.3645557
BibTeX
Elenter, J., Chamon, L. F. O., & Ribeiro, A. (2024, Mai). Near-Optimal Solutions of Constrained Learning Problems.
Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7-11, 2024, Austria.
https://doi.org/10.48550/arXiv.2403.11844
BibTeX
Elenter, J., Chamon, L. F. O., & Ribeiro, A. (2024, Mai). Near-Optimal Solutions of Constrained Learning Problems.
Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7-11, 2024, Austria.
https://doi.org/10.48550/arXiv.2403.11844
BibTeX
Errica, F., & Niepert, M. (2024, Mai). Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks.
Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7-11, 2024, Austria.
https://doi.org/10.48550/arXiv.2305.10544
BibTeX
Errica, F., & Niepert, M. (2024, Mai). Tractable Probabilistic Graph Representation Learning with Graph-Induced Sum-Product Networks.
Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7-11, 2024, Austria.
https://doi.org/10.48550/arXiv.2305.10544
BibTeX
Hagnberger, J., Kalimuthu, M., Musekamp, D., & Niepert, M. (2024, Mai). Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent PDEs. Proceedings of the AI4DifferentialEquations in Science workshop at ICLR 2024, May 7-11, 2024, Austria.
BibTeX
Hagnberger, J., Kalimuthu, M., Musekamp, D., & Niepert, M. (2024, Mai). Vectorized Conditional Neural Fields: A Framework for Solving Time-dependent PDEs. Proceedings of the AI4DifferentialEquations in Science workshop at ICLR 2024, May 7-11, 2024, Austria.
BibTeX
Liu, A., Niepert, M., & den Broeck, G. V. (2024, Mai). Image Inpainting via Tractable Steering of Diffusion Models.
Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7--11, 2024, Austria.
https://doi.org/10.48550/arXiv.2401.03349
BibTeX
Liu, A., Niepert, M., & den Broeck, G. V. (2024, Mai). Image Inpainting via Tractable Steering of Diffusion Models.
Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7-11, 2024, Austria.
https://doi.org/10.48550/arXiv.2401.03349
BibTeX
Qian, C., Manolache, A., Ahmed, K., Zeng, Z., den Broeck, G. V., Niepert, M., & Morris, C. (2024, Mai). Probabilistically Rewired Message-Passing Neural Networks.
Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7--11, 2024, Austria.
https://doi.org/10.48550/arXiv.2310.02156
BibTeX
Qian, C., Manolache, A., Ahmed, K., Zeng, Z., den Broeck, G. V., Niepert, M., & Morris, C. (2024, Mai). Probabilistically Rewired Message-Passing Neural Networks.
Proceedings of the International Conference on Learning Representations(ICLR 2024), May 7-11, 2024, Austria.
https://doi.org/10.48550/arXiv.2310.02156
BibTeX
Seifer, P., Hernández, D., Lämmel, R., & Staab, S. (2024, Mai). From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries.
Proceedings of the ACM Web Conference 2024 (WWW ’24), May13--17, 2024, Singapore, Singapore. WWW ’24, Singapore.
https://doi.org/10.1145/3589334.3645550
BibTeX
Seifer, P., Hernández, D., Lämmel, R., & Staab, S. (2024, Mai). From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries.
Proceedings of the ACM Web Conference 2024 (WWW ’24), May13--17, 2024, Singapore, Singapore. WWW ’24, Singapore.
https://doi.org/10.1145/3589334.3645550
BibTeX
Tran, H.-C., Nguyen, D. M. H., Nguyen, M.-D., Le, N. H., & T. Nguyen, B. (2024, Mai). Energy Minimizing-based Token Merging for Accelerating Transformers. Proceedings of Practical ML for Low Resource Settings in Science Workshop at ICLR 2024, May 7-11, 2024, Austria.
BibTeX
Tran, H.-C., Nguyen, D. M. H., Nguyen, M.-D., Le, N. H., & T. Nguyen, B. (2024, Mai). Energy Minimizing-based Token Merging for Accelerating Transformers. Proceedings of Practical ML for Low Resource Settings in Science Workshop at ICLR 2024, May 7-11, 2024, Austria.
BibTeX
Zubaria, A., Hernández, D., Galárraga, L., Flouris, G., Fundulaki, I., & Hose, K. (2024, Mai). NPCS: Native Provenance Computation for SPARQL.
Proceedings of the ACM Web Conference 2024 (WWW ’24), May13--17, 2024, Singapore, Singapore. WWW ’24, Singapore.
https://doi.org/10.1145/3589334.3645557
BibTeX
Zubaria, A., Hernández, D., Galárraga, L., Flouris, G., Fundulaki, I., & Hose, K. (2024, Mai). NPCS: Native Provenance Computation for SPARQL.
Proceedings of the ACM Web Conference 2024 (WWW ’24), May13--17, 2024, Singapore, Singapore. WWW ’24, Singapore.
https://doi.org/10.1145/3589334.3645557
BibTeX
Zubaria, A., Hernández, D., Galárraga, L., Flouris, G., Fundulaki, I., & Hose, K. (2024, Mai). NPCS: Native Provenance Computation for SPARQL.
Proceedings of the ACM Web Conference 2024 (WWW ’24), May13--17, 2024, Singapore, Singapore. WWW ’24, Singapore.
https://doi.org/10.1145/3589334.3645557
BibTeX
Asma, Z., Hernandez, D., Galárraga, L., Flouris, G., Fundulaki, I., & Hose, K. (2024).
Code and benchmark for NPCS, a Native Provenance Computation for SPARQL.
https://doi.org/10.18419/darus-3973
BibTeX
Asma, Z., Hernandez, D., Galárraga, L., Flouris, G., Fundulaki, I., & Hose, K. (2024).
Code and benchmark for NPCS, a Native Provenance Computation for SPARQL.
https://doi.org/10.18419/darus-3973
BibTeX
Ding, Z., Cai, H., Wu, J., Ma, Y., Liao, R., Xiong, B., & Tresp, V. (2024). zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models.
Annual Conference of the North American Chapter of the Association for Computational Linguistics.
https://arxiv.org/abs/2311.10112
BibTeX
Pan, J., Nayyeri, M., Li, Y., & Staab, S. (2024). HGE: Embedding Temporal Knowledge Graphs in a Product Space of Heterogeneous Geometric Subspaces. Thirty-eighth Conference on Artificial Intelligence, AAAI, 2024, Vancouver, Canada, February 22 – February 25, 2024,.
BibTeX
Pan, J., Nayyeri, M., Li, Y., & Staab, S. (2024). HGE: Embedding Temporal Knowledge Graphs in a Product Space of Heterogeneous Geometric Subspaces. Thirty-eighth Conference on Artificial Intelligence, AAAI, 2024, Vancouver, Canada, February 22 – February 25, 2024,.
BibTeX
Peng, K., Yin, C., Zheng, J., Liu, R., Schneider, D., Zhang, J., Yang, K., Sarfraz, M. S., Stiefelhagen, R., & Roitberg, A. (2024). Navigating Open Set Scenarios for Skeleton-based Action Recognition.
The 38th Annual AAAI Conference on Artificial Intelligence.
https://arxiv.org/abs/2312.06330
BibTeX
Peng, K., Yin, C., Zheng, J., Liu, R., Schneider, D., Zhang, J., Yang, K., Sarfraz, M. S., Stiefelhagen, R., & Roitberg, A. (2024). Navigating Open Set Scenarios for Skeleton-based Action Recognition.
The 38th Annual AAAI Conference on Artificial Intelligence.
https://arxiv.org/abs/2312.06330
BibTeX
Potyka, N., Zhu, Y., He, Y., Kharlamov, E., & Staab, S. (2024). Robust Knowledge Extraction from Large Language Models using Social Choice Theory.
In Proceedings of the 23rd International Conference on Autonomous Agents and Multi-Agent Systems.
https://arxiv.org/abs/2312.14877
BibTeX
Potyka, N., Zhu, Y., He, Y., Kharlamov, E., & Staab, S. (2024). Robust Knowledge Extraction from Large Language Models using Social Choice Theory.
In Proceedings of the 23rd International Conference on Autonomous Agents and Multi-Agent Systems.
https://arxiv.org/abs/2312.14877
BibTeX
Potyka, N., Zhu, Y., He, Y., Kharlamov, E., & Staab, S. (2024). Robust Knowledge Extraction from Large Language Models using Social Choice Theory.
The 23rd International Conference on Autonomous Agents and Multi-Agent Systems.
https://arxiv.org/abs/2312.14877
BibTeX
Schwindt, S., Meisinger, L., Negreiros, B., Schneider, T., & Nowak, W. (2024). Transfer learning achieves high recall for object classification in fluvial environments with limited data.
Geomorphology,
455, 109185.
https://doi.org/10.1016/j.geomorph.2024.109185
BibTeX
Seifer, P., Hernández, D., Lämmel, R., & Staab, S. (2024). From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries. Proceedings of the ACM Web Conference 2024, WWW 2024, Singapore, 13 - 17 May 2024.
BibTeX
Seifer, P., Hernández, D., Lämmel, R., & Staab, S. (2024). From Shapes to Shapes: Inferring SHACL Shapes for Results of SPARQL CONSTRUCT Queries.
WWW ’24: The ACM Web Conference 2024 Proceedings. WWW ’24, Singapore.
https://doi.org/10.1145/3589334.3645550
BibTeX
BibTeX
Tan, Y., Lv, H., Zhou, Z., Guo, W., Xiong, B., Liu, W., Chen, C., Wang, S., & Yang, C. (2024). Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation.
The 40th IEEE International Conference on Data Engineering.
http://www.cs.emory.edu/~jyang71/files/logirec.pdf
BibTeX
Xiong, B., Nayyeri, M., Luo, L., Wang, Z., Pan, S., & Staab, S. (2024). NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning.
The 38th Annual AAAI Conference on Artificial Intelligence.
https://arxiv.org/abs/2312.09219
BibTeX
Xiong, B., Nayyeri, M., Luo, L., Wang, Z., Pan, S., & Staab, S. (2024). NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning.
The 38th Annual AAAI Conference on Artificial Intelligence.
https://arxiv.org/abs/2312.09219
BibTeX
Zaverkin, V., Holzmüller, D., Christiansen, H., Errica, F., Alesiani, F., Takamoto, M., Niepert, M., & Kästner, J. (2024). Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials.
npj Comput. Mater.,
10(1), Article 1.
https://doi.org/10.1038/s41524-024-01254-1
BibTeX
Zaverkin, V., Holzmüller, D., Christiansen, H., Errica, F., Alesiani, F., Takamoto, M., Niepert, M., & Kästner, J. (2024). Uncertainty-biased molecular dynamics for learning uniformly accurate interatomic potentials.
npj Comput. Mater.,
10(1), Article 1.
https://doi.org/10.1038/s41524-024-01254-1
BibTeX
Zubaria, A., Hernández, D., Galárraga, L., Flouris, G., Fundulaki, I., & Hose, K. (2024). NPCS: Native Provenance Computation for SPARQL. Proceedings of the ACM Web Conference 2024, WWW 2024, Singapore, 13 - 17 May 2024.
BibTeX
Zubaria, A., Hernández, D., Galárraga, L., Flouris, G., Fundulaki, I., & Hose, K. (2024). NPCS: Native Provenance Computation for SPARQL. Proceedings of the ACM Web Conference 2024, WWW 2024, Singapore, 13 - 17 May 2024.
BibTeX