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pro vyhledávání: '"Gastinger, A."'
Autor:
Gastinger, Julia, Huang, Shenyang, Galkin, Mikhail, Loghmani, Erfan, Parviz, Ali, Poursafaei, Farimah, Danovitch, Jacob, Rossi, Emanuele, Koutis, Ioannis, Stuckenschmidt, Heiner, Rabbany, Reihaneh, Rabusseau, Guillaume
Multi-relational temporal graphs are powerful tools for modeling real-world data, capturing the evolving and interconnected nature of entities over time. Recently, many novel models are proposed for ML on such graphs intensifying the need for robust
Externí odkaz:
http://arxiv.org/abs/2406.09639
Autor:
Gastinger, Julia, Meilicke, Christian, Errica, Federico, Sztyler, Timo, Schuelke, Anett, Stuckenschmidt, Heiner
Temporal Knowledge Graph (TKG) Forecasting aims at predicting links in Knowledge Graphs for future timesteps based on a history of Knowledge Graphs. To this day, standardized evaluation protocols and rigorous comparison across TKG models are availabl
Externí odkaz:
http://arxiv.org/abs/2404.16726
Autor:
Kotnis, Bhushan, Gashteovski, Kiril, Gastinger, Julia, Serra, Giuseppe, Alesiani, Francesco, Sztyler, Timo, Shaker, Ammar, Gong, Na, Lawrence, Carolin, Xu, Zhao
With Human-Centric Research (HCR) we can steer research activities so that the research outcome is beneficial for human stakeholders, such as end users. But what exactly makes research human-centric? We address this question by providing a working de
Externí odkaz:
http://arxiv.org/abs/2207.04447
We present a novel methodology to build powerful predictive process models. Our method, denoted ProcK (Process & Knowledge), relies not only on sequential input data in the form of event logs, but can learn to use a knowledge graph to incorporate inf
Externí odkaz:
http://arxiv.org/abs/2109.04881
Autor:
Zehentmayr, Franz a, ⁎, Feurstein, Petra b, Ruznic, Elvis a, Langer, Brigitte b, Grambozov, Brane a, Klebermass, Marisa b, Hüpfel, Herbert c, Feichtinger, Johann d, Minasch, Danijela e, Heilmann, Martin f, Breitfelder, Barbara g, Steffal, Claudia h, Gastinger-Grass, Gisela i, Kirchhammer, Karoline j, Kazil, Margit k, Stranzl, Heidi l, Dieckmann, Karin f
Publikováno v:
In Radiotherapy and Oncology July 2024 196
The contribution of this work is twofold: (1) We introduce a collection of ensemble methods for time series forecasting to combine predictions from base models. We demonstrate insights on the power of ensemble learning for forecasting, showing experi
Externí odkaz:
http://arxiv.org/abs/2104.11475
Automated algorithm selection and hyperparameter tuning facilitates the application of machine learning. Traditional multi-armed bandit strategies look to the history of observed rewards to identify the most promising arms for optimizing expected tot
Externí odkaz:
http://arxiv.org/abs/2001.11261
Autor:
Schmidt, Mischa, Safarani, Shahd, Gastinger, Julia, Jacobs, Tobias, Nicolas, Sebastien, Schülke, Anett
Automated hyperparameter tuning aspires to facilitate the application of machine learning for non-experts. In the literature, different optimization approaches are applied for that purpose. This paper investigates the performance of Differential Evol
Externí odkaz:
http://arxiv.org/abs/1904.06960
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