| Composition-based multi-relational graph convolutional networks S Vashishth, S Sanyal, V Nitin, P Talukdar arXiv preprint arXiv:1911.03082, 2019 | 1526 | 2019 |
| Never-ending Learning T Mitchell, W Cohen, E Hruschka, P Talukdar, B Yang, J Betteridge, ... Communications of the ACM 61 (5), 103-115, 2018 | 1501 | 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 | 1501 | 2015 |
| Gemini 2.5: Pushing the frontier with advanced reasoning, multimodality, long context, and next generation agentic capabilities G Comanici, E Bieber, M Schaekermann, I Pasupat, N Sachdeva, I Dhillon, ... arXiv preprint arXiv:2507.06261, 2025 | 1337 | 2025 |
| 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 | 766 | 2019 |
| Improving multi-hop question answering over knowledge graphs using knowledge base embeddings A Saxena, A Tripathi, P Talukdar ACL 2020, 4498-4507, 2020 | 750 | 2020 |
| Hyte: Hyperplane-based temporally aware knowledge graph embedding SS Dasgupta, SN Ray, P Talukdar Proceedings of the 2018 conference on empirical methods in natural language …, 2018 | 645 | 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 | 515 | 2020 |
| 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 | 468 | 2020 |
| Muril: Multilingual representations for indian languages S Khanuja, D Bansal, S Mehtani, S Khosla, A Dey, B Gopalan, ... arXiv preprint arXiv:2103.10730, 2021 | 441 | 2021 |
| 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 | 387 | 2014 |
| 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 | 323 | 2018 |
| Kvqa: Knowledge-aware visual question answering S Shah, A Mishra, N Yadati, PP Talukdar Proceedings of the AAAI conference on artificial intelligence 33 (01), 8876-8884, 2019 | 322 | 2019 |
| New regularized algorithms for transductive learning PP Talukdar, K Crammer Joint European Conference on Machine Learning and Knowledge Discovery in …, 2009 | 311 | 2009 |
| Learning Effective and Interpretable Semantic Models using Non-Negative Sparse Embedding B Murphy, P Talukdar, T Mitchell International Conference on Computational Linguistics (COLING), 2012 | 251 | 2012 |
| 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 | 247 | 2014 |
| A re-evaluation of knowledge graph completion methods Z Sun, S Vashishth, S Sanyal, P Talukdar, Y Yang Proceedings of the 58th annual meeting of the association for computational …, 2020 | 228 | 2020 |
| NILE: Natural Language Inference with Faithful Natural Language Explanations S Kumar, P Talukdar ACL 2020, 2020 | 218 | 2020 |
| Question answering over temporal knowledge graphs A Saxena, S Chakrabarti, P Talukdar arXiv preprint arXiv:2106.01515, 2021 | 217 | 2021 |
| Graph-based semi-supervised learning A Subramanya, PP Talukdar Morgan & Claypool Publishers, 2014 | 214 | 2014 |
Tom MitchellFounders University Professor of Machine Learning, Carnegie Mellon UniversityVerified email at cs.cmu.edu
