| graph2vec: Learning distributed representations of graphs A Narayanan, M Chandramohan, R Venkatesan, L Chen, Y Liu, S Jaiswal arXiv preprint arXiv:1707.05005, 2017 | 820 | 2017 |
| TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNs S Jaiswal, B Fernando, C Tan European Conference on Computer Vision, 259-276, 2022 | 8 | 2022 |
| Revealing the illusion of joint multimodal understanding in videoqa models IS Rawal, S Jaiswal, B Fernando, C Tan arXiv preprint arXiv:2306.08889, 2023 | 3 | 2023 |
| What do CNNs gain by imitating the visual development of primate infants? S Jaiswal, D Choi, B Fernando BMVC, 2020 | 1 | 2020 |
| Are VideoQA Models Truly Multimodal? I Rawal, S Jaiswal, B Fernando, C Tan XAI in Action: Past, Present, and Future Applications, 2023 | | 2023 |
| The Path to AGI Goes through Embodiment C Tan, S Jaiswal Proceedings of the AAAI Symposium Series 1 (1), 104-108, 2023 | | 2023 |
| A Probabilistic-Logic based Commonsense Representation Framework for Modelling Inferences with Multiple Antecedents and Varying Likelihoods S Jaiswal, L Yan, D Choi, K Kwok arXiv preprint arXiv:2211.16822, 2022 | | 2022 |
| How does simulating aspects of primate infant visual development inform training of CNNs? S Jaiswal, D Choi, B Fernando CogSci, 2020 | | 2020 |
| Learning to reason iteratively and parallelly for complex visual reasoning S Jaiswal, D Roy, B Fernando, C Tan | | |
| Dissecting Zero-Shot Visual Reasoning Capabilities in Vision and Language Models A Nagar, S Jaiswal, C Tan | | |
| Supplement to TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNs S Jaiswal, B Fernando, C Tan | | |
