| Why m heads are better than one: Training a diverse ensemble of deep networks S Lee, S Purushwalkam, M Cogswell, D Crandall, D Batra arXiv preprint arXiv:1511.06314, 2015 | 295 | 2015 |
| Demystifying contrastive self-supervised learning: Invariances, augmentations and dataset biases S Purushwalkam, A Gupta Advances in Neural Information Processing Systems 33, 3407-3418, 2020 | 191 | 2020 |
| Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles S Lee, S Purushwalkam, M Cogswell, V Ranjan, D Crandall, D Batra Advances In Neural Information Processing Systems, 2119-2127, 2016 | 190 | 2016 |
| Task-driven modular networks for zero-shot compositional learning S Purushwalkam, M Nickel, A Gupta, MA Ranzato Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 133 | 2019 |
| The unsurprising effectiveness of pre-trained vision models for control S Parisi, A Rajeswaran, S Purushwalkam, A Gupta International Conference on Machine Learning, 17359-17371, 2022 | 100 | 2022 |
| Applying machine learning to identify autistic adults using imitation: An exploratory study B Li, A Sharma, J Meng, S Purushwalkam, E Gowen PloS one 12 (8), e0182652, 2017 | 84 | 2017 |
| Audio-visual floorplan reconstruction S Purushwalkam, SVA Gari, VK Ithapu, C Schissler, P Robinson, A Gupta, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 38 | 2021 |
| Pose from action: Unsupervised learning of pose features based on motion S Purushwalkam, A Gupta arXiv preprint arXiv:1609.05420, 2016 | 29 | 2016 |
| Bounce and learn: Modeling scene dynamics with real-world bounces S Purushwalkam, A Gupta, DM Kaufman, B Russell arXiv preprint arXiv:1904.06827, 2019 | 24 | 2019 |
| Combining the best of graphical models and convnets for semantic segmentation M Cogswell, X Lin, S Purushwalkam, D Batra arXiv preprint arXiv:1412.4313, 2014 | 23 | 2014 |
| Aligning videos in space and time S Purushwalkam, T Ye, S Gupta, A Gupta Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020 | 22 | 2020 |
| The challenges of continuous self-supervised learning S Purushwalkam, P Morgado, A Gupta European Conference on Computer Vision, 702-721, 2022 | 21 | 2022 |
| Long sequence modeling with xgen: A 7b llm trained on 8k input sequence length E Nijkamp, T Xie, H Hayashi, B Pang, C Xia, C Xing, J Vig, S Yavuz, ... Salesforce AI Research Blog, 2023 | 10 | 2023 |
| Automatic segmentation of adipose tissue from thigh magnetic resonance images S Purushwalkam, B Li, Q Meng, J McPhee Image Analysis and Recognition: 10th International Conference, ICIAR 2013 …, 2013 | 7 | 2013 |
| The functional correspondence problem Z Lai, S Purushwalkam, A Gupta Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021 | 6 | 2021 |
| Xgen-7b technical report E Nijkamp, T Xie, H Hayashi, B Pang, C Xia, C Xing, J Vig, S Yavuz, ... arXiv preprint arXiv:2309.03450, 2023 | 3 | 2023 |
| Diffusion Model Alignment Using Direct Preference Optimization B Wallace, M Dang, R Rafailov, L Zhou, A Lou, S Purushwalkam, S Ermon, ... arXiv preprint arXiv:2311.12908, 2023 | | 2023 |
| ConRad: Image Constrained Radiance Fields for 3D Generation from a Single Image S Purushwalkam, N Naik arXiv preprint arXiv:2311.05230, 2023 | | 2023 |
| Leveraging Inexpensive Supervision Signals for Visual Learning SPS Prakash Carnegie Mellon University Pittsburgh, 2017 | | 2017 |
| Audio-Visual Floorplan Reconstruction Supplementary Material S Purushwalkam, SVA Gari, VK Ithapu, C Schissler, P Robinson, A Gupta, ... | | |
Abhinav GuptaAssociate Professor, Robotics Institute, Carnegie Mellon University在 cs.cmu.edu 的電子郵件地址已通過驗證
