| Composition-based multi-relational graph convolutional networks S Vashishth*, S Sanyal*, V Nitin, P Talukdar Proceedings of International Conference on Learning Representations 2020 (ICLR), 2019 | 897 | 2019 |
| InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactions S Vashishth*, S Sanyal*, V Nitin, N Agrawal, P Talukdar Proceedings of Thiry-Fourth AAAI conference on Artificial Intelligence (AAAI …, 2019 | 359 | 2019 |
| ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations E Ranjan, S Sanyal, PP Talukdar | 305 | 2020 |
| Generate rather than Retrieve: Large Language Models are Strong Context Generators W Yu, D Iter, S Wang, Y Xu, M Ju, S Sanyal, C Zhu, M Zeng, M Jiang arXiv preprint arXiv:2209.10063, 2022 | 186 | 2022 |
| Faith and fate: Limits of transformers on compositionality N Dziri, X Lu, M Sclar, XL Li, L Jiang, BY Lin, S Welleck, P West, ... Advances in Neural Information Processing Systems 36, 2024 | 175 | 2024 |
| A Reevaluation of Knowledge Graph Completion Methods Z Sun, S Vashishth, S Sanyal, P Talukdar, Y Yang arXiv preprint arXiv:1911.03903, 2019 | 173 | 2019 |
| MT-CGCNN: Integrating Crystal Graph Convolutional Neural Network with Multitask Learning for Material Property Prediction S Sanyal, J Balachandran, N Yadati, A Kumar, P Rajagopalan, S Sanyal, ... arXiv preprint arXiv:1811.05660, 2018 | 53 | 2018 |
| ProteinGCN: Protein model quality assessment using graph convolutional networks S Sanyal, I Anishchenko, A Dagar, D Baker, P Talukdar URL https://doi. org/10.1101/2020.04 6, 388, 2020 | 51 | 2020 |
| Discretized Integrated Gradients for Explaining Language Models S Sanyal, X Ren Proceedings of the 2021 Conference on Empirical Methods in Natural Language …, 2021 | 42 | 2021 |
| FaiRR: Faithful and Robust Deductive Reasoning over Natural Language S Sanyal, H Singh, X Ren Proceedings of the 60th Annual Meeting of the Association for Computational …, 2022 | 28 | 2022 |
| RobustLR: A Diagnostic Benchmark for Evaluating Logical Robustness of Deductive Reasoners S Sanyal, Z Liao, X Ren Proceedings of the 2022 Conference on Empirical Methods in Natural Language …, 2022 | 13* | 2022 |
| PlaSma: Procedural Knowledge Models for Language-based Planning and Re-Planning F Brahman, C Bhagavatula, V Pyatkin, JD Hwang, XL Li, HJ Arai, ... The Twelfth International Conference on Learning Representations, 2023 | 11* | 2023 |
| SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning A Chan, J Xu, B Long, S Sanyal, T Gupta, X Ren Advances in Neural Information Processing Systems 34, 2021 | 7* | 2021 |
| APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning S Sanyal, Y Xu, S Wang, Z Yang, R Pryzant, W Yu, C Zhu, X Ren arXiv preprint arXiv:2212.09282, 2022 | 4 | 2022 |
| SCORE: A framework for Self-Contradictory Reasoning Evaluation Z Liu, I Lee, Y Du, S Sanyal, J Zhao arXiv preprint arXiv:2311.09603, 2023 | 3 | 2023 |
| Are Machines Better at Complex Reasoning? Unveiling Human-Machine Inference Gaps in Entailment Verification S Sanyal, T Xiao, J Liu, W Wang, X Ren arXiv e-prints, arXiv: 2402.03686, 2024 | 2* | 2024 |
| Potential energy surface prediction of Alumina polymorphs using graph neural network S Sanyal, AK Sagotra, N Kumar, S Rathi, M Krishna, N Somayajula, ... arXiv preprint arXiv:2301.12059, 2023 | 1 | 2023 |
Partha TalukdarGoogle Research and IISc Bangalore在 iisc.ac.in 的电子邮件经过验证
