| 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 | 108* | 2021 |
| Sum-of-Squares Polynomial Flow P Jaini, KA Selby, Y Yu International Conference of Machine Learning, ICML 2019, 2019 | 100 | 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 | 90 | 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 | 63 | 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 | 32 | 2017 |
| 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 | 27 | 2016 |
| Tails of Triangular Flows P Jaini, I Kobyzev, M Brubaker, Y Yu ICML 2020 arXiv:1907.04481, 2020 | 26* | 2020 |
| Linking normative models of natural tasks to descriptive models of neural response P Jaini, J Burge Journal of Vision 17 (12), 16-16, 2017 | 23 | 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 | 20 | 2018 |
| 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 | 19 | 2017 |
| 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 | 10 | 2021 |
| 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 | 10 | 2016 |
| Deep homogeneous mixture models: representation, separation, and approximation P Jaini, P Poupart, Y Yu Advances in Neural Information Processing Systems 31, 2018 | 9 | 2018 |
| 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 | 5 | 2021 |
| A Positivstellensatz for conditional SAGE signomials AH Wang, P Jaini, Y Yu, P Poupart arXiv preprint arXiv:2003.03731, 2020 | 5 | 2020 |
| Yaoliang Yu P Jaini, KA Selby Sum-of-squares polynomial flow 2, 2019 | 5 | 2019 |
| Self normalizing flows TA Keller, JWT Peters, P Jaini, E Hoogeboom, P Forré, M Welling International Conference on Machine Learning, 5378-5387, 2021 | 4 | 2021 |
| Argmax Flows: Learning Categorical Distributions with Normalizing Flows E Hoogeboom, D Nielsen, P Jaini, P Forré, M Welling Third Symposium on Advances in Approximate Bayesian Inference, 2021 | 3 | 2021 |
| Device and method for training a normalizing flow using self-normalized gradients J Peters, TA Keller, A Khoreva, E Hoogeboom, M Welling, P Forre, P Jaini US Patent App. 17/448,126, 2022 | 1 | 2022 |
| Learning directed acyclic graph SPNs in sub-quadratic time A Ghose, P Jaini, P Poupart International Journal of Approximate Reasoning 120, 48-73, 2020 | 1 | 2020 |
Max WellingProfessor Machine Learning, University of AmsterdamVerified email at uva.nl
