| Diversity driven attention model for query-based abstractive summarization P Nema, M Khapra, A Laha, B Ravindran Association for Computational Linguistics (ACL), 2017 | 170 | 2017 |
| Story generation from sequence of independent short descriptions P Jain, P Agrawal, A Mishra, M Sukhwani, A Laha, K Sankaranarayanan SIGKDD Workshop on Machine Learning for Creativity (ML4Creativity), 2017 | 88 | 2017 |
| Unsupervised Neural Text Simplification S Surya, A Mishra, A Laha, P Jain, S Karthik Proceedings of the 57th Annual Meeting of the Association for Computational …, 2019 | 83 | 2019 |
| On Controllable Sparse Alternatives to Softmax A Laha, SA Chemmengath, P Agrawal, MM Khapra, K Sankaranarayanan, ... Neural Information Processing Systems (NIPS), 2018 | 43 | 2018 |
| Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization P Nema, S Shetty, P Jain, A Laha, K Sankaranarayanan, MM Khapra North American Chapter of the Association for Computational Linguistics …, 2018 | 35 | 2018 |
| Joint Learning of Correlated Sequence Labelling Tasks Using Bidirectional Recurrent Neural Networks V Pahuja, A Laha, S Mirkin, V Raykar, L Kotlerman, G Lev INTERSPEECH, 2017 | 35 | 2017 |
| A Mixed Hierarchical Attention based Encoder-Decoder Approach for Standard Table Summarization P Jain, A Laha, K Sankaranarayanan, P Nema, MM Khapra, S Shetty North American Chapter of the Association for Computational Linguistics …, 2018 | 28 | 2018 |
| An empirical evaluation of various deep learning architectures for bi-sequence classification tasks A Laha, V Raykar International Conference on Computational Linguistics (COLING), 2016 | 23 | 2016 |
| Scalable Micro-planned Generation of Discourse from Structured Data A Laha, P Jain, A Mishra, K Sankaranarayanan Computational Linguistics, 2019 | 17 | 2019 |
| Storytelling from structured data and knowledge graphs: An nlg perspective A Mishra, A Laha, K Sankaranarayanan, P Jain, S Krishnan Proceedings of the 57th annual meeting of the association for computational …, 2019 | 8 | 2019 |
| A Machine Learning Approach for Evaluating Creative Artifacts D Shrivastava, SA CG, A Laha, K Sankaranarayanan SIGKDD Workshop on Machine Learning for Creativity (ML4Creativity), 2017 | 8 | 2017 |
| Generation of variable natural language descriptions from structured data A Mishra, P Jain, A Laha, K Sankaranarayanan US Patent 10,776,579, 2020 | 5 | 2020 |
| Object storage and retrieval based upon context V Ekambaram, S Kalyanaraman, A Laha US Patent 10,713,485, 2020 | 3 | 2020 |
| Displaying dynamic content on multiple devices A Laha, V Ekambaram, P Jain, D Shrivastava US Patent 10,664,217, 2020 | 2 | 2020 |
| Real-time assessment of text consistency A Mishra, A Laha, P Jain, K Sankaranarayanan US Patent 11,194,964, 2021 | 1 | 2021 |
| An Efficient Cryptographic Hash Algorithm (BSA) S Mukherjee, B Roy, A Laha National Workshop on Cryptology, 2012 | 1 | 2012 |
| Cognitive assistant for co-generating creative content A Sankaran, P Lohia, P Agrawal, D Shrivastava, A Laha, P Jain US Patent 11,301,640, 2022 | | 2022 |
| Structured article generation P Jain, A Mishra, A Laha, S Krishnan, K Sankaranarayanan US Patent 11,194,816, 2021 | | 2021 |
| Script modification S Chemmengath, P Jain, A Laha, S Krishnan US Patent 10,891,437, 2021 | | 2021 |
| What is deemed computationally creative? S Agrawal, A Sankaran, A Laha, SA Chemmengath, D Shrivastava, ... IBM Journal of Research and Development 63 (1), 3: 1-3: 12, 2019 | | 2019 |
Karthik SankaranarayananMicrosoftVerified email at microsoft.com
