Priyank Jaini

According to our database, Priyank Jaini authored at least 27 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Decoupling Semantic Similarity from Spatial Alignment for Neural Networks.

Tassilo Wald, Constantin Ulrich, Gregor Köhler, David Zimmerer, Stefan Denner, Michael Baumgartner, Fabian Isensee, Priyank Jaini, Klaus H. Maier-Hein

CoRR, 2024

Towards flexible perception with visual memory.

Robert Geirhos, Priyank Jaini, Austin Stone, Sourabh Medapati, Xi Yi, George Toderici, Abhijit Ogale, Jonathon Shlens

CoRR, 2024

Intriguing Properties of Generative Classifiers.

Priyank Jaini, Kevin Clark, Robert Geirhos

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths.

Lars Holdijk, Yuanqi Du, Ferry Hooft, Priyank Jaini, Bernd Ensing, Max Welling

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Text-to-Image Diffusion Models are Zero Shot Classifiers.

Kevin Clark, Priyank Jaini

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Recent Advancements in Tractable Probabilistic Inference (Dagstuhl Seminar 22161).

Priyank Jaini, Kristian Kersting, Antonio Vergari, Max Welling

Dagstuhl Reports, 2022

Path Integral Stochastic Optimal Control for Sampling Transition Paths.

Lars Holdijk, Yuanqi Du, Ferry Hooft, Priyank Jaini, Bernd Ensing, Max Welling

CoRR, 2022

2021

Particle Dynamics for Learning EBMs.

Kirill Neklyudov, Priyank Jaini, Max Welling

CoRR, 2021

Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models.

Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling

CoRR, 2021

Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent.

Priyank Jaini, Lars Holdijk, Max Welling

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions.

Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Self Normalizing Flows.

T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forré, Max Welling

Proceedings of the 38th International Conference on Machine Learning, 2021

Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC.

Priyank Jaini, Didrik Nielsen, Max Welling

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Learning directed acyclic graph SPNs in sub-quadratic time.

Amur Ghose, Priyank Jaini, Pascal Poupart

Int. J. Approx. Reason., 2020

Complete Hierarchy of Relaxation for Constrained Signomial Positivity.

Allen Houze Wang, Priyank Jaini, Yaoliang Yu, Pascal Poupart

CoRR, 2020

SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows.

Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, Max Welling

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Tails of Lipschitz Triangular Flows.

Priyank Jaini, Ivan Kobyzev, Yaoliang Yu, Marcus A. Brubaker

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Likelihood-based Density Estimation using Deep Architectures.

Priyank Jaini

PhD thesis, 2019

Tails of Triangular Flows.

Priyank Jaini, Ivan Kobyzev, Marcus A. Brubaker, Yaoliang Yu

CoRR, 2019

Sum-of-Squares Polynomial Flow.

Priyank Jaini, Kira A. Selby, Yaoliang Yu

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Prometheus : Directly Learning Acyclic Directed Graph Structures for Sum-Product Networks.

Priyank Jaini, Amur Ghose, Pascal Poupart

Proceedings of the International Conference on Probabilistic Graphical Models, 2018

Deep Homogeneous Mixture Models: Representation, Separation, and Approximation.

Priyank Jaini, Pascal Poupart, Yaoliang Yu

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Accuracy Maximization Analysis for Sensory-Perceptual Tasks: Computational Improvements, Filter Robustness, and Coding Advantages for Scaled Additive Noise.

Johannes Burge, Priyank Jaini

PLoS Comput. Biol., 2017

Online Bayesian Transfer Learning for Sequential Data Modeling.

Priyank Jaini, Zhitang Chen, Pablo Carbajal, Edith Law, Laura Middleton, Kayla Regan, Mike Schaekermann, George Trimponias, James Tung, Pascal Poupart

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Online and Distributed learning of Gaussian mixture models by Bayesian Moment Matching.

Priyank Jaini, Pascal Poupart

CoRR, 2016

Online Algorithms for Sum-Product Networks with Continuous Variables.

Priyank Jaini, Abdullah Rashwan, Han Zhao, Yue Liu, Ershad Banijamali, Zhitang Chen, Pascal Poupart

Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016

Online flow size prediction for improved network routing.

Pascal Poupart, Zhitang Chen, Priyank Jaini, Fred Fung, Hengky Susanto, Yanhui Geng, Li Chen, Kai Chen, Hao Jin

Proceedings of the 24th IEEE International Conference on Network Protocols, 2016