• case-insensitive prefix search: default
  e.g., sig matches "SIGIR" as well as "signal"
• exact word search: append dollar sign ($) to word
  e.g., graph$ matches "graph", but not "graphics"
• boolean and: separate words by space
  e.g., codd model
• boolean or: connect words by pipe symbol (|)
  e.g., graph|network

Update May 7, 2017: Please note that we had to disable the phrase search operator (.) and the boolean not operator (-) due to technical problems. For the time being, phrase search queries will yield regular prefix search result, and search terms preceded by a minus will be interpreted as regular (positive) search terms.

found 65 matches

• 2024
• 
  Timo Bertram, Johannes Fürnkranz, Martin Müller:
  Learning With Generalised Card Representations for "Magic: The Gathering". CoG 2024: 1-8
• 
  Timo Bertram, Johannes Fürnkranz, Martin Müller:
  Efficiently Training Neural Networks for Imperfect Information Games by Sampling Information Sets. KI 2024: 17-29
• 
  Timo Bertram, Johannes Fürnkranz, Martin Müller:
  Neural Network-based Information Set Weighting for Playing Reconnaissance Blind Chess. CoRR abs/2407.05864 (2024)
• 
  Timo Bertram, Johannes Fürnkranz, Martin Müller:
  Efficiently Training Neural Networks for Imperfect Information Games by Sampling Information Sets. CoRR abs/2407.05876 (2024)
• 
  Timo Bertram, Johannes Fürnkranz, Martin Müller:
  Learning With Generalised Card Representations for "Magic: The Gathering". CoRR abs/2407.05879 (2024)
• 
  Timo Bertram, Johannes Fürnkranz, Martin Müller:
  Contrastive Learning of Preferences with a Contextual InfoNCE Loss. CoRR abs/2407.05898 (2024)
• 2023
• 
  Christopher Diehl, Timo Sievernich, Martin Krüger, Frank Hoffmann, Torsten Bertram:
  Uncertainty-Aware Model-Based Offline Reinforcement Learning for Automated Driving. IEEE Robotics Autom. Lett. 8(2): 1167-1174 (2023)
• 
  Timo Bertram, Johannes Fürnkranz, Martin Müller:
  Weighting Information Sets with Siamese Neural Networks in Reconnaissance Blind Chess. CoG 2023: 1-8
• 
  Christopher Diehl, Tobias Klosek, Martin Krüger, Nils Murzyn, Timo Osterburg, Torsten Bertram:
  Energy-based Potential Games for Joint Motion Forecasting and Control. CoRL 2023: 3112-3141
• 
  Christopher Diehl, Tobias Klosek, Martin Krüger, Nils Murzyn, Timo Osterburg, Torsten Bertram:
  Energy-based Potential Games for Joint Motion Forecasting and Control. CoRR abs/2312.01811 (2023)
• 2022
• 
  Martin Krüger, Patrick Palmer, Christopher Diehl, Timo Osterburg, Torsten Bertram:
  Recognition Beyond Perception: Environmental Model Completion by Reasoning for Occluded Vehicles. IEEE Robotics Autom. Lett. 7(4): 10999-11006 (2022)
• 
  Timo Bertram, Johannes Fürnkranz, Martin Müller:
  Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess. CoG 2022: 608-611
• 
  Timo Bertram, Johannes Fürnkranz, Martin Müller:
  Quantity vs Quality: Investigating the Trade-Off between Sample Size and Label Reliability. CoRR abs/2204.09462 (2022)
• 
  Timo Bertram, Johannes Fürnkranz, Martin Müller:
  Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess. CoRR abs/2208.02029 (2022)
• 2021
• 
  Timo Bertram, Johannes Fürnkranz, Martin Müller:
  Predicting Human Card Selection in Magic: The Gathering with Contextual Preference Ranking. CoG 2021: 1-8
• 
  Timothy M. McPhillips, Thomas Thelen, Craig Willis, Kacper Kowalik, Matthew B. Jones, Bertram Ludäscher:
  CPR-A Comprehensible Provenance Record for Verification Workflows in Whole Tale. IPAW 2021: 263-269
• 
  Nikolaus Nova Parulian, Timothy M. McPhillips, Bertram Ludäscher:
  A Model and System for Querying Provenance from Data Cleaning Workflows. IPAW 2021: 183-197
• 
  Ryan W. Gardner, Gino Perrotta, Anvay Shah, Shivaram Kalyanakrishnan, Kevin A. Wang, Gregory Clark, Timo Bertram, Johannes Fürnkranz, Martin Müller, Brady P. Garrison, Prithviraj Dasgupta, Saeid Rezaei:
  The Machine Reconnaissance Blind Chess Tournament of NeurIPS 2022. NeurIPS (Competition and Demos) 2021: 119-132
• 
  George Alter, Jack Gager, Pascal Heus, Carson Hunter, Sanda Ionescu, Jeremy Iverson, H. V. Jagadish, Bertram Ludäscher, Jared Lyle, Timothy M. McPhillips, Alexander Mueller, Sigve Nordgaard, Ørnulf Risnes, Dan J. Smith, Jie Song, Thomas Thelen:
  Detailed Provenance Metadata from Statistical Analysis Software: TaPP Applications Track. TaPP 2021
• 
  Rafael Ferreira da Silva, Henri Casanova, Kyle Chard, Dan Laney, Dong H. Ahn, Shantenu Jha, Carole A. Goble, Lavanya Ramakrishnan, J. Luc Peterson, Bjoern Enders, Douglas Thain, Ilkay Altintas, Yadu N. Babuji, Rosa M. Badia, Vivien Bonazzi, Tainã Coleman, Michael R. Crusoe, Ewa Deelman, Frank Di Natale, Paolo Di Tommaso, Thomas Fahringer, Rosa Filgueira, Grigori Fursin, Alex Ganose, Björn A. Grüning, Daniel S. Katz, Olga A. Kuchar, Ana Kupresanin, Bertram Ludäscher, Ketan Maheshwari, Marta Mattoso, Kshitij Mehta, Todd S. Munson, Jonathan Ozik, Tom Peterka, Loic Pottier, Tim Randles, Stian Soiland-Reyes, Benjamín Tovar, Matteo Turilli, Thomas D. Uram, Karan Vahi, Michael Wilde, Matthew Wolf, Justin M. Wozniak:
  Workflows Community Summit: Bringing the Scientific Workflows Community Together. CoRR abs/2103.09181 (2021)
• 
  Timo Bertram, Johannes Fürnkranz, Martin Müller:
  Predicting Human Card Selection in Magic: The Gathering with Contextual Preference Ranking. CoRR abs/2105.11864 (2021)
• 
  Timo Bertram, Johannes Fürnkranz, Martin Müller:
  A Comparison of Contextual and Non-Contextual Preference Ranking for Set Addition Problems. CoRR abs/2107.04438 (2021)
• 
  Christopher Diehl, Timo Sievernich, Martin Krüger, Frank Hoffmann, Torsten Bertram:
  UMBRELLA: Uncertainty-Aware Model-Based Offline Reinforcement Learning Leveraging Planning. CoRR abs/2111.11097 (2021)
• 2020
• 
  Timo Schick, Hinrich Schütze:
  BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance. ACL 2020: 3996-4007
• 
  Kyle Chard, Niall Gaffney, Mihael Hategan, Kacper Kowalik, Bertram Ludäscher, Timothy M. McPhillips, Jarek Nabrzyski, Victoria Stodden, Ian J. Taylor, Thomas Thelen, Matthew J. Turk, Craig Willis:
  Toward Enabling Reproducibility for Data-Intensive Research using the Whole Tale Platform. CoRR abs/2005.06087 (2020)
• 2019
• 
  Kyle Chard, Thomas Thelen, Matthew J. Turk, Craig Willis, Niall Gaffney, Matthew B. Jones, Kacper Kowalik, Bertram Ludäscher, Timothy M. McPhillips, Jarek Nabrzyski, Victoria Stodden, Ian J. Taylor:
  Application of BagIt-Serialized Research Object Bundles for Packaging and Re-Execution of Computational Analyses. eScience 2019: 514-521
• 
  Timothy M. McPhillips, Craig Willis, Michael R. Gryk, Santiago Núñez Corrales, Bertram Ludäscher:
  Reproducibility by Other Means: Transparent Research Objects. eScience 2019: 502-509
• 
  Kyle Chard, Niall Gaffney, Mihael Hategan, Kacper Kowalik, Bertram Ludäscher, Timothy M. McPhillips, Jarek Nabrzyski, Victoria Stodden, Ian J. Taylor, Thomas Thelen, Matthew J. Turk, Craig Willis:
  Toward Enabling Reproducibility for Data-Intensive Research Using the Whole Tale Platform. PARCO 2019: 766-778
• 
  Timothy M. McPhillips, Lan Li, Nikolaus Nova Parulian, Bertram Ludäscher:
  Modeling Provenance and Understanding Reproducibility for OpenRefine Data Cleaning Workflows. TaPP 2019
• 
  Timo Schick, Hinrich Schütze:
  BERTRAM: Improved Word Embeddings Have Big Impact on Contextualized Model Performance. CoRR abs/1910.07181 (2019)

skipping 35 more matches