Priyank Jaini
Priyank Jaini
Verified email at - Homepage
Cited by
Cited by
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
Sum-of-Squares Polynomial Flow
P Jaini, KA Selby, Y Yu
International Conference of Machine Learning, ICML 2019, 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
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
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
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
Tails of Triangular Flows
P Jaini, I Kobyzev, M Brubaker, Y Yu
ICML 2020 arXiv:1907.04481, 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
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
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
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
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
Deep homogeneous mixture models: representation, separation, and approximation
P Jaini, P Poupart, Y Yu
Advances in Neural Information Processing Systems 31, 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
A Positivstellensatz for conditional SAGE signomials
AH Wang, P Jaini, Y Yu, P Poupart
arXiv preprint arXiv:2003.03731, 2020
Yaoliang Yu
P Jaini, KA Selby
Sum-of-squares polynomial flow 2, 2019
Self normalizing flows
TA Keller, JWT Peters, P Jaini, E Hoogeboom, P Forré, M Welling
International Conference on Machine Learning, 5378-5387, 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
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
Learning directed acyclic graph SPNs in sub-quadratic time
A Ghose, P Jaini, P Poupart
International Journal of Approximate Reasoning 120, 48-73, 2020
The system can't perform the operation now. Try again later.
Articles 1–20