| Never-ending Learning T Mitchell, W Cohen, E Hruschka, P Talukdar, B Yang, J Betteridge, ... Communications of the ACM 61 (5), 103-115, 2018 | 1080 | 2018 |
| Never-Ending Learning T Mitchell, W Cohen, E Hruschka Jr, P Talukdar, J Betteridge, A Carlson, ... Twenty-Ninth Conference on Artificial Intelligence (AAAI), 2015 | 1080 | 2015 |
| Composition-based multi-relational graph convolutional networks S Vashishth, S Sanyal, V Nitin, P Talukdar arXiv preprint arXiv:1911.03082, 2019 | 455 | 2019 |
| New regularized algorithms for transductive learning PP Talukdar, K Crammer Machine Learning and Knowledge Discovery in Databases: European Conference …, 2009 | 286 | 2009 |
| Simultaneously uncovering the patterns of brain regions involved in different story reading subprocesses L Wehbe, B Murphy, P Talukdar, A Fyshe, A Ramdas, T Mitchell PloS one 9 (11), e112575, 2014 | 265 | 2014 |
| Improving multi-hop question answering over knowledge graphs using knowledge base embeddings A Saxena, A Tripathi, P Talukdar ACL 2020, 4498-4507, 2020 | 258 | 2020 |
| HyTE: Hyperplane-based Temporally aware Knowledge Graph Embedding. SS Dasgupta, SN Ray, PP Talukdar EMNLP, 2001-2011, 2018 | 250 | 2018 |
| Hypergcn: A new method for training graph convolutional networks on hypergraphs N Yadati, M Nimishakavi, P Yadav, V Nitin, A Louis, P Talukdar Advances in neural information processing systems 32, 2019 | 240* | 2019 |
| Reside: Improving distantly-supervised neural relation extraction using side information S Vashishth, R Joshi, SS Prayaga, C Bhattacharyya, P Talukdar arXiv preprint arXiv:1812.04361, 2018 | 230 | 2018 |
| Interacte: Improving convolution-based knowledge graph embeddings by increasing feature interactions S Vashishth, S Sanyal, V Nitin, N Agrawal, P Talukdar Proceedings of the AAAI conference on artificial intelligence 34 (03), 3009-3016, 2020 | 191 | 2020 |
| Incorporating vector space similarity in random walk inference over knowledge bases M Gardner, P Talukdar, J Krishnamurthy, T Mitchell Proceedings of the 2014 conference on empirical methods in natural language …, 2014 | 190 | 2014 |
| Learning Effective and Interpretable Semantic Models using Non-Negative Sparse Embedding B Murphy, P Talukdar, T Mitchell International Conference on Computational Linguistics (COLING), 2012 | 183 | 2012 |
| Weakly-supervised acquisition of labeled class instances using graph random walks P Talukdar, J Reisinger, M Pasca, D Ravichandran, R Bhagat, F Pereira Proceedings of the 2008 Conference on Empirical Methods in Natural Language …, 2008 | 168 | 2008 |
| Graph-based semi-supervised learning A Subramanya, PP Talukdar Synthesis Lectures on Artificial Intelligence and Machine Learning 8 (4), 1-125, 2014 | 165 | 2014 |
| Asap: Adaptive structure aware pooling for learning hierarchical graph representations E Ranjan, S Sanyal, P Talukdar Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5470-5477, 2020 | 150 | 2020 |
| FlexiFaCT: Scalable Flexible Factorization of Coupled Tensors on Hadoop A Beutel, PP Talukdar, A Kumar, C Faloutsos, EE Papalexakis, EP Xing Proceedings of the 2014 SIAM international conference on data mining, 109-117, 2014 | 144 | 2014 |
| Automatic code assignment to medical text K Crammer, M Dredze, K Ganchev, P Talukdar, S Carroll Biological, translational, and clinical language processing, 129-136, 2007 | 138 | 2007 |
| The orchestra collaborative data sharing system ZG Ives, TJ Green, G Karvounarakis, NE Taylor, V Tannen, PP Talukdar, ... ACM Sigmod Record 37 (3), 26-32, 2008 | 126 | 2008 |
| Improving learning and inference in a large knowledge-base using latent syntactic cues M Gardner, P Talukdar, B Kisiel, T Mitchell Proceedings of the 2013 Conference on Empirical Methods in Natural Language …, 2013 | 121 | 2013 |
| A re-evaluation of knowledge graph completion methods Z Sun, S Vashishth, S Sanyal, P Talukdar, Y Yang arXiv preprint arXiv:1911.03903, 2019 | 117 | 2019 |
Tom MitchellFounders University Professor of Machine Learning, Carnegie Mellon UniversityVerified email at cs.cmu.edu
