| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 407 | 2023 |
| Repository-level prompt generation for large language models of code D Shrivastava, H Larochelle, D Tarlow International Conference on Machine Learning, 31693-31715, 2023 | 54 | 2023 |
| Learning to combine per-example solutions for neural program synthesis D Shrivastava, H Larochelle, D Tarlow Advances in Neural Information Processing Systems 34, 6102-6114, 2021 | 14 | 2021 |
| Minimax and neyman-pearson meta-learning for outlier languages EM Ponti, R Aralikatte, D Shrivastava, S Reddy, A Søgaard arXiv preprint arXiv:2106.01051, 2021 | 13 | 2021 |
| A Data and Model-Parallel, Distributed and Scalable Framework for Training of Deep Networks in Apache Spark D Shrivastava, S Chaudhury, D Jayadeva arXiv preprint arXiv:1708.05840, 2017 | 11 | 2017 |
| 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 | 10 | 2017 |
| RepoFusion: Training Code Models to Understand Your Repository D Shrivastava, D Kocetkov, H de Vries, D Bahdanau, T Scholak arXiv preprint arXiv:2306.10998v1, 2023 | 9* | 2023 |
| Displaying dynamic content on multiple devices A Laha, V Ekambaram, P Jain, D Shrivastava US Patent 10,664,217, 2020 | 8 | 2020 |
| On-the-Fly Adaptation of Source Code Models D Shrivastava, H Larochelle, D Tarlow NeurIPS Workshop on Computer-Assisted Programming, 2020 | 7* | 2020 |
| System and method to implement a cognitive quit smoking assistant PS Dayama, D Shrivastava US Patent App. 15/811,964, 2019 | 6 | 2019 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 4 | 2024 |
| Modeling topical coherence in discourse without supervision D Shrivastava, A Mishra, K Sankaranarayanan arXiv preprint arXiv:1809.00410, 2018 | 3 | 2018 |
| Approach intelligent writing assistants usability with seven stages of action A Bhat, D Shrivastava, JLC Guo arXiv preprint arXiv:2304.02822, 2023 | 2 | 2023 |
| A Design Space for Intelligent and Interactive Writing Assistants M Lee, KI Gero, JJY Chung, SB Shum, V Raheja, H Shen, S Venugopalan, ... arXiv preprint arXiv:2403.14117, 2024 | 1 | 2024 |
| Transfer Learning by Modeling a Distribution over Policies D Shrivastava, EG Dhekane, R Islam ICML Workshop on Multi-Task and Lifelong Reinforcement Learning, 2019 | 1 | 2019 |
| Contextual cues for deep learning models of code D Shrivastava | | 2023 |
| Automated constraint extraction and testing S Kenkre, SRK Penubothula, D Shrivastava, HG Ramaswamy, V Pandit US Patent 11,354,502, 2022 | | 2022 |
| 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 |
| Automated constraint extraction and testing S Kenkre, SRK Penubothula, D Shrivastava, HG Ramaswamy, V Pandit US Patent 10,902,200, 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 |
Hugo LarochelleGoogle DeepMind & MilaVerified email at google.com
