| Leveraging abstract meaning representation for knowledge base question answering P Kapanipathi, I Abdelaziz, S Ravishankar, S Roukos, A Gray, R Astudillo, ... arXiv preprint arXiv:2012.01707, 2020 | 104* | 2020 |
| Explanations for commonsenseqa: New dataset and models S Aggarwal, D Mandowara, V Agrawal, D Khandelwal, P Singla, D Garg Proceedings of the 59th Annual Meeting of the Association for Computational …, 2021 | 65 | 2021 |
| The techqa dataset V Castelli, R Chakravarti, S Dana, A Ferritto, R Florian, M Franz, D Garg, ... arXiv preprint arXiv:1911.02984, 2019 | 30 | 2019 |
| Translucent answer predictions in multi-hop reading comprehension GPS Bhargav, M Glass, D Garg, S Shevade, S Dana, D Khandelwal, ... Proceedings of the AAAI conference on artificial intelligence 34 (05), 7700-7707, 2020 | 12 | 2020 |
| SYGMA: system for generalizable modular question answering overknowledge bases S Neelam, U Sharma, H Karanam, S Ikbal, P Kapanipathi, I Abdelaziz, ... arXiv preprint arXiv:2109.13430, 2021 | 10 | 2021 |
| Lazy Generic Cuts Dinesh Khandelwal, Kush Bhatia, Chetan Arora, Parag Singla Computer Vision and Image Understanding 143, 80-91, 2016 | 9* | 2016 |
| A deep neural approach to kgqa via sparql silhouette generation S Purkayastha, S Dana, D Garg, D Khandelwal, GPS Bhargav 2022 International Joint Conference on Neural Networks (IJCNN), 1-8, 2022 | 5 | 2022 |
| A benchmark for generalizable and interpretable temporal question answering over knowledge bases S Neelam, U Sharma, H Karanam, S Ikbal, P Kapanipathi, I Abdelaziz, ... arXiv preprint arXiv:2201.05793, 2022 | 5 | 2022 |
| Knowledge graph question answering via SPARQL silhouette generation S Purkayastha, S Dana, D Garg, D Khandelwal, GP Bhargav arXiv preprint arXiv:2109.09475, 2021 | 5 | 2021 |
| Inductive quantum embedding SK Srivastava, D Khandelwal, D Madan, D Garg, H Karanam, ... Advances in Neural Information Processing Systems 33, 16012-16024, 2020 | 5 | 2020 |
| Max-margin feature selection Y Prasad, D Khandelwal, KK Biswas Pattern Recognition Letters 95, 51-57, 2017 | 5 | 2017 |
| Zero-shot Entity Linking with Less Data GPS Bhargav, D Khandelwal, S Dana, D Garg, P Kapanipathi, S Roukos, ... Findings of the Association for Computational Linguistics: NAACL 2022, 1681-1697, 2022 | 3 | 2022 |
| Targeted extraction of temporal facts from textual resources for improved temporal question answering over knowledge bases N Kannen, U Sharma, S Neelam, D Khandelwal, S Ikbal, H Karanam, ... arXiv preprint arXiv:2203.11054, 2022 | 1 | 2022 |
| Exploiting test time evidence to improve predictions of deep neural networks D Khandelwal, S Agrawal, P Singla, C Arora CoRR, 2018 | 1 | 2018 |
| Zero-shot entity linking based on symbolic information D Khandelwal, GPS Bhargav, D Saswati, D Garg, PK Bangalore, ... US Patent App. 17/575,951, 2023 | | 2023 |
| Image Manipulation via Multi-Hop Instructions--A New Dataset and Weakly-Supervised Neuro-Symbolic Approach H Singh, P Garg, M Gupta, K Shah, AK Mondal, D Khandelwal, P Singla, ... arXiv preprint arXiv:2305.14410, 2023 | | 2023 |
| AI4Code S Tamilselvam, D Khandelwal, S Dana, P Kumar, D Garg Asian Conference on Machine Learning, 2022 | | 2022 |
| Image Manipulation via Neuro-Symbolic Networks H Singh, P Garg, M Gupta, K Shah, AK Mondal, D Khandelwal, P Singla, ... NeurIPS 2022 Workshop on Neuro Causal and Symbolic AI (nCSI), 2022 | | 2022 |
| Weakly Supervised Neuro-Symbolic Image Manipulation via Multi-Hop Complex Instructions H Singh, P Garg, M Gupta, K Shah, AK Mondal, D Khandelwal, P Singla, ... | | 2022 |
| Semantic Answer Type Prediction using Dense Type Embeddings GPS Bhargav, D Khandelwal, D Garg, S Dana SeMantic Answer Type and Relation Prediction Task at ISWC Semantic Web Challenge, 2021 | | 2021 |
Dinesh GargIBM India Research Lab, Bangalore, IndiaVerified email at in.ibm.com
