XLNet: Generalized Autoregressive Pretraining for Language Understanding Z Yang arXiv preprint arXiv:1906.08237, 2019 | 9916 | 2019 |
Transformer-xl: Attentive language models beyond a fixed-length context Z Dai arXiv preprint arXiv:1901.02860, 2019 | 4256 | 2019 |
The use of MMR, diversity-based reranking for reordering documents and producing summaries J Carbonell, J Goldstein Proceedings of the 21st annual international ACM SIGIR conference on …, 1998 | 3950 | 1998 |
Machine learning: An artificial intelligence approach RS Michalski Springer Science & Business Media, 2013 | 3889* | 2013 |
Topic detection and tracking pilot study final report J Allan, JG Carbonell, G Doddington, J Yamron, Y Yang Carnegie Mellon University, 1998 | 1562 | 1998 |
Extracting information from textual documents in the electronic health record: a review of recent research SM Meystre, GK Savova, KC Kipper-Schuler, JF Hurdle Yearbook of medical informatics 17 (01), 128-144, 2008 | 1121 | 2008 |
A study of retrospective and on-line event detection Y Yang, T Pierce, J Carbonell Proceedings of the 21st annual international ACM SIGIR conference on …, 1998 | 1085 | 1998 |
An overview of machine learning JG Carbonell, RS Michalski, TM Mitchell Machine learning, 3-23, 1983 | 1010 | 1983 |
Derivational analogy: A theory of reconstructive problem solving and expertise acquisition JG Carbonell Carnegie Mellon University, 1985 | 945 | 1985 |
Temporal collaborative filtering with bayesian probabilistic tensor factorization L Xiong, X Chen, TK Huang, J Schneider, JG Carbonell Proceedings of the 2010 SIAM international conference on data mining, 211-222, 2010 | 875 | 2010 |
Learning by Analogy: Formulating and Generalizing Plans from Past Experience JG Carbonell Springer Berlin Heidelberg, 1983 | 837 | 1983 |
Summarizing text documents: Sentence selection and evaluation metrics J Goldstein, M Kantrowitz, V Mittal, J Carbonell Proceedings of the 22nd annual international ACM SIGIR conference on …, 1999 | 720 | 1999 |
Multi-document summarization by sentence extraction J Goldstein, VO Mittal, JG Carbonell, M Kantrowitz NAACL-ANLP 2000 workshop: automatic summarization, 2000 | 575 | 2000 |
Integrating planning and learning: The PRODIGY architecture M Veloso, J Carbonell, A Perez, D Borrajo, E Fink, J Blythe Journal of Experimental & Theoretical Artificial Intelligence 7 (1), 81-120, 1995 | 560 | 1995 |
Characterizing and avoiding negative transfer Z Wang, Z Dai, B Póczos, J Carbonell Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 547 | 2019 |
Learning approaches for detecting and tracking news events Y Yang, JG Carbonell, RD Brown, T Pierce, BT Archibald, X Liu IEEE Intelligent Systems and their Applications 14 (4), 32-43, 1999 | 538 | 1999 |
Machine learning: a guide to current research TM Mitchell, JG Carbonell, RS Michalski Springer Science & Business Media, 1986 | 481 | 1986 |
LEAP: A learning apprentice for VLSI design TM Mitchell, S Mabadevan, LI Steinberg Machine learning, 271-289, 1990 | 442 | 1990 |
Explanation-based learning: A problem solving perspective S Minton, JG Carbonell, CA Knoblock, DR Kuokka, O Etzioni, Y Gil Artificial Intelligence 40 (1-3), 63-118, 1989 | 436 | 1989 |
A discriminative graph-based parser for the abstract meaning representation J Flanigan, S Thomson, JG Carbonell, C Dyer, NA Smith Proceedings of the 52nd Annual Meeting of the Association for Computational …, 2014 | 396 | 2014 |