| Sparse Bayesian learning for basis selection DP Wipf, BD Rao IEEE Transactions on Signal processing 52 (8), 2153-2164, 2004 | 1682 | 2004 |
| An empirical Bayesian strategy for solving the simultaneous sparse approximation problem DP Wipf, BD Rao IEEE Transactions on Signal Processing 55 (7), 3704-3716, 2007 | 994 | 2007 |
| Iterative ReweightedandMethods for Finding Sparse Solutions D Wipf, S Nagarajan IEEE Journal of Selected Topics in Signal Processing 4 (2), 317-329, 2010 | 594 | 2010 |
| Diagnosing and enhancing VAE models B Dai, D Wipf arXiv preprint arXiv:1903.05789, 2019 | 470 | 2019 |
| A new view of automatic relevance determination D Wipf, S Nagarajan Advances in neural information processing systems 20, 2007 | 462 | 2007 |
| A unified Bayesian framework for MEG/EEG source imaging D Wipf, S Nagarajan NeuroImage 44 (3), 947-966, 2009 | 412 | 2009 |
| Lane change intent analysis using robust operators and sparse bayesian learning JC McCall, DP Wipf, MM Trivedi, BD Rao IEEE Transactions on Intelligent Transportation Systems 8 (3), 431-440, 2007 | 383 | 2007 |
| A generic deep architecture for single image reflection removal and image smoothing Q Fan, J Yang, G Hua, B Chen, D Wipf Proceedings of the IEEE International Conference on Computer Vision, 3238-3247, 2017 | 361 | 2017 |
| Latent variable Bayesian models for promoting sparsity DP Wipf, BD Rao, S Nagarajan IEEE Transactions on Information Theory 57 (9), 6236-6255, 2011 | 340 | 2011 |
| A practical transfer learning algorithm for face verification X Cao, D Wipf, F Wen, G Duan, J Sun Proceedings of the IEEE international conference on computer vision, 3208-3215, 2013 | 255 | 2013 |
| Robust Bayesian estimation of the location, orientation, and time course of multiple correlated neural sources using MEG DP Wipf, JP Owen, HT Attias, K Sekihara, SS Nagarajan NeuroImage 49 (1), 641-655, 2010 | 252 | 2010 |
| Variational EM algorithms for non-Gaussian latent variable models J Palmer, K Kreutz-Delgado, B Rao, D Wipf Advances in neural information processing systems 18, 2005 | 242 | 2005 |
| From canonical correlation analysis to self-supervised graph neural networks H Zhang, Q Wu, J Yan, D Wipf, PS Yu Advances in Neural Information Processing Systems 34, 76-89, 2021 | 232 | 2021 |
| Unsupervised extraction of video highlights via robust recurrent auto-encoders H Yang, B Wang, S Lin, D Wipf, M Guo, B Guo Proceedings of the IEEE international conference on computer vision, 4633-4641, 2015 | 212 | 2015 |
| NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification Q Wu, W Zhao, Z Li, D Wipf, J Yan Advances in Neural Information Processing Systems, 2022 | 209 | 2022 |
| Multi-image blind deblurring using a coupled adaptive sparse prior H Zhang, D Wipf, Y Zhang Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2013 | 203 | 2013 |
| Compressing neural networks using the variational information bottleneck B Dai, C Zhu, B Guo, D Wipf International Conference on Machine Learning, 1135-1144, 2018 | 197 | 2018 |
| Robust photometric stereo using sparse regression S Ikehata, D Wipf, Y Matsushita, K Aizawa 2012 IEEE Conference on Computer Vision and Pattern Recognition, 318-325, 2012 | 197 | 2012 |
| Handling distribution shifts on graphs: An invariance perspective Q Wu, H Zhang, J Yan, D Wipf International Conference on Learning Representations, 2022 | 194 | 2022 |
| Maximal sparsity with deep networks? B Xin, Y Wang, W Gao, B Wang, D Wipf Advances in Neural Information Processing Systems, 4340-4348, 2016 | 193 | 2016 |
