| VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning A Srivastava, L Valkov, C Russell, M Gutmann, C Sutton 31st Conference on Neural Information Processing Systems (NIPS 2017), Long …, 2017 | 711 | 2017 |
| Autoencoding Variational Inference for Topic Models A Srivastava, C Sutton International Conference on Learning Representations (ICLR), 2017 | 553 | 2017 |
| Fast and scalable Bayesian deep learning by weight-perturbation in Adam ME Khan, D Nielsen, V Tangkaratt, W Lin, Y Gal, A Srivastava International Conference on Machine Learning, 2018, 2018 | 262 | 2018 |
| Equivariant self-supervised learning: Encouraging equivariance in representations R Dangovski, L Jing, C Loh, S Han, A Srivastava, B Cheung, P Agrawal, ... International Conference on Learning Representations, 2021 | 82* | 2021 |
| Houdini: Lifelong learning as program synthesis L Valkov, D Chaudhari, A Srivastava, C Sutton, S Chaudhuri Advances in neural information processing systems 31, 2018 | 79 | 2018 |
| Variational russian roulette for deep bayesian nonparametrics K Xu, A Srivastava, C Sutton International Conference on Machine Learning, 6963-6972, 2019 | 19 | 2019 |
| A bayesian-symbolic approach to reasoning and learning in intuitive physics K Xu, A Srivastava, D Gutfreund, F Sosa, T Ullman, J Tenenbaum, ... Advances in Neural Information Processing Systems 34, 2478-2490, 2021 | 17 | 2021 |
| Neural variational inference for topic models A Srivastava, C Sutton Bayesian deep learning workshop, NIPS 2016, 2016 | 17 | 2016 |
| Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference CL Hurwitz, K Xu, A Srivastava, AP Buccino, M Hennig NeurIPS, 2019, 2019 | 15 | 2019 |
| Targeted neural dynamical modeling C Hurwitz, A Srivastava, K Xu, J Jude, M Perich, L Miller, M Hennig Advances in Neural Information Processing Systems 34, 29379-29392, 2021 | 13 | 2021 |
| Generative Ratio Matching Networks A Srivastava, MU Gutmann, K Xu, C Sutton International Conference on Learning Representations, 2020 | 12* | 2020 |
| Clustering with a reject option: Interactive clustering as bayesian prior elicitation A Srivastava, J Zou, C Sutton KDD 2016 Workshop on Interactive Data Exploration and Analytics (IDEA’16 …, 2016 | 12 | 2016 |
| Links: A dataset of a hundred million planar linkage mechanisms for data-driven kinematic design A Heyrani Nobari, A Srivastava, D Gutfreund, F Ahmed International Design Engineering Technical Conferences and Computers and …, 2022 | 9 | 2022 |
| Variational inference in pachinko allocation machines A Srivastava, C Sutton arXiv preprint arXiv:1804.07944, 2018 | 9 | 2018 |
| Beyond statistical similarity: Rethinking metrics for deep generative models in engineering design L Regenwetter, A Srivastava, D Gutfreund, F Ahmed arXiv preprint arXiv:2302.02913, 2023 | 8 | 2023 |
| Identifiability guarantees for causal disentanglement from soft interventions J Zhang, C Squires, K Greenewald, A Srivastava, K Shanmugam, C Uhler arXiv preprint arXiv:2307.06250, 2023 | 6 | 2023 |
| Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression A Srivastava, S Han, K Xu, B Rhodes, MU Gutmann arXiv preprint arXiv:2305.00869, 2023 | 6 | 2023 |
| not-so-biggan: Generating high-fidelity images on a small compute budget S Han, A Srivastava, C Hurwitz, P Sattigeri, DD Cox arXiv preprint arXiv:2009.04433 2, 2020 | 6 | 2020 |
| Improving Negative-Prompt Inversion via Proximal Guidance L Han, S Wen, Q Chen, Z Zhang, K Song, M Ren, R Gao, Y Chen, D Liu, ... arXiv preprint arXiv:2306.05414, 2023 | 5 | 2023 |
| Improving the reconstruction of disentangled representation learners via multi-stage modelling A Srivastava, Y Bansal, Y Ding, C Hurwitz, K Xu, B Egger, P Sattigeri, ... arXiv preprint arXiv:2010.13187, 2020 | 4 | 2020 |
Charles SuttonGoogle, University of EdinburghVerified email at inf.ed.ac.uk
