| Argmax flows and multinomial diffusion: Learning categorical distributions E Hoogeboom, D Nielsen, P Jaini, P Forré, M Welling Advances in neural information processing systems 34, 12454-12465, 2021 | 481* | 2021 |
| Sum-of-Squares Polynomial Flow P Jaini, KA Selby, Y Yu International Conference of Machine Learning, ICML 2019, 2019 | 161 | 2019 |
| Online flow size prediction for improved network routing P Poupart, Z Chen, P Jaini, F Fung, H Susanto, Y Geng, L Chen, K Chen, ... 2016 IEEE 24th International Conference on Network Protocols (ICNP), 1-6, 2016 | 134 | 2016 |
| Survae flows: Surjections to bridge the gap between vaes and flows D Nielsen, P Jaini, E Hoogeboom, O Winther, M Welling Advances in Neural Information Processing Systems 33, 12685-12696, 2020 | 116 | 2020 |
| Text-to-image diffusion models are zero shot classifiers K Clark, P Jaini Advances in Neural Information Processing Systems 36, 58921-58937, 2023 | 107 | 2023 |
| Tails of Triangular Flows P Jaini, I Kobyzev, M Brubaker, Y Yu ICML 2020 arXiv:1907.04481, 2020 | 59* | 2020 |
| Accuracy Maximization Analysis for Sensory-Perceptual Tasks: Computational Improvements, Filter Robustness, and Coding Advantages for Scaled Additive Noise J Burge, P Jaini Public Library of Science - PLoS Computational Biology, 2017 | 39 | 2017 |
| Intriguing properties of generative classifiers P Jaini, K Clark, R Geirhos arXiv preprint arXiv:2309.16779, 2023 | 37 | 2023 |
| Online algorithms for sum-product networks with continuous variables P Jaini, A Rashwan, H Zhao, Y Liu, E Banijamali, Z Chen, P Poupart Conference on probabilistic graphical models, 228-239, 2016 | 31 | 2016 |
| Linking normative models of natural tasks to descriptive models of neural response P Jaini, J Burge Journal of Vision 17 (12), 16-16, 2017 | 28 | 2017 |
| Online Bayesian transfer learning for sequential data modeling P Jaini, Z Chen, P Carbajal, E Law, L Middleton, K Regan, ... International Conference on Learning Representations, ICLR, 2017 | 25 | 2017 |
| Prometheus: Directly learning acyclic directed graph structures for sum-product networks P Jaini, A Ghose, P Poupart International conference on probabilistic graphical models, 181-192, 2018 | 22 | 2018 |
| Self normalizing flows TA Keller, JWT Peters, P Jaini, E Hoogeboom, P Forré, M Welling International Conference on Machine Learning, 5378-5387, 2021 | 19 | 2021 |
| Learning equivariant energy based models with equivariant stein variational gradient descent P Jaini, L Holdijk, M Welling Advances in Neural Information Processing Systems 34, 16727-16737, 2021 | 15 | 2021 |
| Deep homogeneous mixture models: representation, separation, and approximation P Jaini, P Poupart, Y Yu Advances in Neural Information Processing Systems 31, 2018 | 14 | 2018 |
| Online and distributed learning of gaussian mixture models by bayesian moment matching P Jaini, P Poupart Symposium on Advances in Approximate Bayesian Inference (AABI), NeurIPS 2017, 2016 | 13 | 2016 |
| Path integral stochastic optimal control for sampling transition paths L Holdijk, Y Du, P Jaini, F Hooft, B Ensing, M Welling ICML 2022 2nd AI for Science Workshop, 2022 | 11 | 2022 |
| Sampling in combinatorial spaces with survae flow augmented mcmc P Jaini, D Nielsen, M Welling International Conference on Artificial Intelligence and Statistics, 3349-3357, 2021 | 11 | 2021 |
| Stochastic optimal control for collective variable free sampling of molecular transition paths L Holdijk, Y Du, F Hooft, P Jaini, B Ensing, M Welling Advances in Neural Information Processing Systems 36, 2024 | 10 | 2024 |
| Exploring exchangeable dataset amortization for bayesian posterior inference S Mittal, NL Bracher, G Lajoie, P Jaini, MA Brubaker ICML 2023 Workshop on Structured Probabilistic Inference {\&} Generative …, 2023 | 8 | 2023 |
Max WellingCAIO CuspAI & Professor Machine Learning, University of AmsterdamVerified email at uva.nl
