| Mr. LDA: A flexible large scale topic modeling package using variational inference in mapreduce K Zhai, J Boyd-Graber, SN Bruch, ML Alkhouja Proceedings of the 21st international conference on World Wide Web, 879-888, 2012 | 209 | 2012 |
| TF-Ranking: Scalable TensorFlow Library for Learning-to-Rank RK Pasumarthi, S Bruch, X Wang, C Li, M Bendersky, M Najork, J Pfeifer, ... Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019 | 169 | 2019 |
| Effectiveness/efficiency tradeoffs for candidate generation in multi-stage retrieval architectures SN Bruch, J Lin Proceedings of the 36th international ACM SIGIR conference on Research and …, 2013 | 125 | 2013 |
| An Analysis of the Softmax Cross Entropy Loss for Learning-to-Rank with Binary Relevance S Bruch, X Wang, M Bendersky, M Najork Proceedings of the 2019 ACM SIGIR International Conference on Theory of …, 2019 | 124 | 2019 |
| Learning Groupwise Multivariate Scoring Functions Using Deep Neural Networks Q Ai, X Wang, S Bruch, N Golbandi, M Bendersky, M Najork | 123 | 2019 |
| Runtime optimizations for tree-based machine learning models SN Bruch, J Lin, AP De Vries IEEE transactions on Knowledge and Data Engineering 26 (9), 2281-2292, 2013 | 99 | 2013 |
| Revisiting Approximate Metric Optimization in the Age of Deep Neural Networks S Bruch, M Zoghi, M Bendersky, M Najork Proceedings of the 42nd International ACM SIGIR Conference on Research and …, 2019 | 96 | 2019 |
| A Stochastic Treatment of Learning to Rank Scoring Functions S Bruch, S Han, M Bendersky, M Najork Proceedings of the 13th ACM International Conference on Web Search and Data …, 2020 | 87 | 2020 |
| An Alternative Cross Entropy Loss for Learning-to-Rank S Bruch Proceedings of The Web Conference 2021 (WWW 2021), 2021 | 65 | 2021 |
| Pseudo test collections for learning web search ranking functions SN Bruch, D Metzler, T Elsayed, J Lin Proceedings of the 34th international ACM SIGIR conference on Research and …, 2011 | 51 | 2011 |
| Fast candidate generation for real-time tweet search with bloom filter chains SN Bruch, J Lin ACM Transactions on Information Systems (TOIS) 31 (3), 1-36, 2013 | 41 | 2013 |
| Document vector representations for feature extraction in multi-stage document ranking SN Bruch, J Lin Information retrieval 16, 747-768, 2013 | 39 | 2013 |
| Training efficient tree-based models for document ranking SN Bruch, J Lin European Conference on Information Retrieval, 146-157, 2013 | 39 | 2013 |
| An Analysis of Fusion Functions for Hybrid Retrieval S Bruch, S Gai, A Ingber ACM Transactions on Information Systems 42 (1), 1-35, 2023 | 37 | 2023 |
| Fast candidate generation for two-phase document ranking: Postings list intersection with Bloom filters SN Bruch, J Lin Proceedings of the 21st ACM international conference on Information and …, 2012 | 29 | 2012 |
| Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library M Guillame-Bert, S Bruch, R Stotz, J Pfeifer Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 25* | 2023 |
| Foundations of Vector Retrieval S Bruch Springer Nature Switzerland, 2024 | 19 | 2024 |
| Efficient and Effective Tree-based and Neural Learning to Rank S Bruch, C Lucchese, FM Nardini Foundations and Trends® in Information Retrieval 17 (1), 1-123, 2023 | 18 | 2023 |
| Dynamic memory allocation policies for postings in real-time twitter search SN Bruch, J Lin, M Busch Proceedings of the 19th ACM SIGKDD international conference on Knowledge …, 2013 | 16 | 2013 |
| Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Representations S Bruch, FM Nardini, C Rulli, R Venturini Proceedings of the 47th International ACM SIGIR Conference on Research and …, 2024 | 15 | 2024 |
Michael BenderskyGoogle DeepMind確認したメール アドレス: google.com