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Results: 122
Number of items: 122
  • Open Access
    Grafberger, S., Guha, S., Groth, P., & Schelter, S. (2023). Mlwhatif: What If You Could Stop Re-Implementing Your Machine Learning Pipeline Analyses over and Over? Proceedings of the VLDB Endowment, 16(12), 4002–4005. https://doi.org/10.14778/3611540.3611606
  • Open Access
    Soiland-Reyes, S., Goble, C., & Groth, P. (2023). Evaluating FAIR Digital Object and Linked Data as distributed object systems. (v1 ed.) ArXiv. https://doi.org/10.48550/arXiv.2306.07436
  • Open Access
    Tamašauskaitė, G., & Groth, P. (2023). Defining a Knowledge Graph Development Process Through a Systematic Review. ACM Transactions on Software Engineering and Methodology, 32(1), Article 27. https://doi.org/10.1145/3522586
  • Open Access
    Ayoughi, M., Mettes, P., & Groth, P. (2023). Self-Contained Entity Discovery from Captioned Videos. ACM Transactions on Multimedia Computing Communications and Applications, 19(5s), Article 177. https://doi.org/10.1145/3583138
  • Open Access
    Allen, B. P., Stork, L., & Groth, P. (2023). Knowledge Engineering using Large Language Models. Transactions on Graph Data and Knowledge, 1(1), Article 3. https://doi.org/10.4230/TGDK.1.1.3
  • Open Access
    Jullien, S., Ariannezhad, M., Groth, P., & de Rijke, M. (2023). A Simulation Environment and Reinforcement Learning Method for Waste Reduction. Transactions on Machine Learning Research, 2023, Article 769. https://openreview.net/forum?id=KSvr8A62MD
  • Open Access
    Prieto, L., Den Boef, J., Groth, P., & Cornelisse, J. (2023). Parameter Efficient Node Classification on Homophilic Graphs. Transactions on Machine Learning Research, 2023, Article 640. https://openreview.net/forum?id=LIT8tjs6rJ
  • Open Access
    Grafberger, S., Groth, P., & Schelter, S. (2023). Automating and Optimizing Data-Centric What-If Analyses on Native Machine Learning Pipelines. Proceedings of the ACM on Management of Data, 1(2), Article 128. https://doi.org/10.1145/3589273
  • Open Access
    Nevin, J., Groth, P., & Lees, M. (2023). Data Integration Landscapes: The Case for Non-optimal Solutions in Network Diffusion Models. In J. Mikyška, C. de Mulatier, M. Paszynski, V. V. Krzhizhanovskaya, J. J. Dongarra, & P. M. A. Sloot (Eds.), Computational Science – ICCS 2023: 23rd International Conference, Prague, Czech Republic, July 3–5, 2023 : proceedings (Vol. I, pp. 494-508). (Lecture Notes in Computer Science; Vol. 14073). Springer. https://doi.org/10.1007/978-3-031-35995-8_35
  • Open Access
    Grafberger, S., Groth, P., & Schelter, S. (2023). Provenance Tracking for End-to-End Machine Learning Pipelines. In The ACM Web Conference 2023: Companion of the World Wide Web Conference WWW 2023 : April 30-May 4, 2023, Austin, Texas, USA (pp. 1512). Association for Computing Machinery. https://doi.org/10.1145/3543873.3587557
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