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ORCIDPriyank Jaini
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2020 – today
- 2024
[c15]Priyank Jaini, Kevin Clark, Robert Geirhos:
Intriguing Properties of Generative Classifiers. ICLR 2024- [i15]Robert Geirhos, Priyank Jaini, Austin Stone, Sourabh Medapati, Xi Yi, George Toderici, Abhijit Ogale, Jonathon Shlens:
Towards flexible perception with visual memory. CoRR abs/2408.08172 (2024) - 2023
- [c14]Kevin Clark, Priyank Jaini:
Text-to-Image Diffusion Models are Zero Shot Classifiers. NeurIPS 2023 - [c13]Lars Holdijk, Yuanqi Du, Ferry Hooft, Priyank Jaini, Bernd Ensing, Max Welling:
Stochastic Optimal Control for Collective Variable Free Sampling of Molecular Transition Paths. NeurIPS 2023 - [i14]Kevin Clark, Priyank Jaini:
Text-to-Image Diffusion Models are Zero-Shot Classifiers. CoRR abs/2303.15233 (2023) - [i13]Priyank Jaini, Kevin Clark, Robert Geirhos:
Intriguing properties of generative classifiers. CoRR abs/2309.16779 (2023) - 2022
- [i12]Lars Holdijk, Yuanqi Du, Ferry Hooft, Priyank Jaini, Bernd Ensing, Max Welling:
Path Integral Stochastic Optimal Control for Sampling Transition Paths. CoRR abs/2207.02149 (2022) - [i11]Priyank Jaini, Kristian Kersting, Antonio Vergari, Max Welling:
Recent Advancements in Tractable Probabilistic Inference (Dagstuhl Seminar 22161). Dagstuhl Reports 12(4): 13-25 (2022) - 2021
- [c12]Priyank Jaini, Didrik Nielsen, Max Welling:
Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC. AISTATS 2021: 3349-3357 - [c11]T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forré, Max Welling:
Self Normalizing Flows. ICML 2021: 5378-5387 - [c10]Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling:
Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions. NeurIPS 2021: 12454-12465 - [c9]Priyank Jaini, Lars Holdijk, Max Welling:
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent. NeurIPS 2021: 16727-16737 - [i10]Priyank Jaini, Didrik Nielsen, Max Welling:
Sampling in Combinatorial Spaces with SurVAE Flow Augmented MCMC. CoRR abs/2102.02374 (2021) - [i9]Emiel Hoogeboom, Didrik Nielsen, Priyank Jaini, Patrick Forré, Max Welling:
Argmax Flows and Multinomial Diffusion: Towards Non-Autoregressive Language Models. CoRR abs/2102.05379 (2021) - [i8]Priyank Jaini, Lars Holdijk, Max Welling:
Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent. CoRR abs/2106.07832 (2021) - [i7]Kirill Neklyudov, Priyank Jaini, Max Welling:
Particle Dynamics for Learning EBMs. CoRR abs/2111.13772 (2021) - 2020
- [j2]Amur Ghose, Priyank Jaini, Pascal Poupart:
Learning directed acyclic graph SPNs in sub-quadratic time. Int. J. Approx. Reason. 120: 48-73 (2020) - [c8]Priyank Jaini, Ivan Kobyzev, Yaoliang Yu, Marcus A. Brubaker:
Tails of Lipschitz Triangular Flows. ICML 2020: 4673-4681 - [c7]Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, Max Welling:
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows. NeurIPS 2020 - [i6]Allen Houze Wang, Priyank Jaini, Yaoliang Yu, Pascal Poupart:
Complete Hierarchy of Relaxation for Constrained Signomial Positivity. CoRR abs/2003.03731 (2020) - [i5]Didrik Nielsen, Priyank Jaini, Emiel Hoogeboom, Ole Winther, Max Welling:
SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows. CoRR abs/2007.02731 (2020) - [i4]T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini, Emiel Hoogeboom, Patrick Forré, Max Welling:
Self Normalizing Flows. CoRR abs/2011.07248 (2020)
2010 – 2019
- 2019
- [b1]Priyank Jaini:
Likelihood-based Density Estimation using Deep Architectures. University of Waterloo, Ontario, Canada, 2019 - [c6]Priyank Jaini, Kira A. Selby, Yaoliang Yu:
Sum-of-Squares Polynomial Flow. ICML 2019: 3009-3018 - [i3]Priyank Jaini, Kira A. Selby, Yaoliang Yu:
Sum-of-Squares Polynomial Flow. CoRR abs/1905.02325 (2019) - [i2]Priyank Jaini, Ivan Kobyzev, Marcus A. Brubaker, Yaoliang Yu:
Tails of Triangular Flows. CoRR abs/1907.04481 (2019) - 2018
- [c5]Priyank Jaini, Pascal Poupart, Yaoliang Yu:
Deep Homogeneous Mixture Models: Representation, Separation, and Approximation. NeurIPS 2018: 7136-7145 - [c4]Priyank Jaini, Amur Ghose, Pascal Poupart:
Prometheus : Directly Learning Acyclic Directed Graph Structures for Sum-Product Networks. PGM 2018: 181-192 - 2017
- [j1]Johannes Burge
, Priyank Jaini:
Accuracy Maximization Analysis for Sensory-Perceptual Tasks: Computational Improvements, Filter Robustness, and Coding Advantages for Scaled Additive Noise. PLoS Comput. Biol. 13(2) (2017) - [c3]Priyank Jaini, Zhitang Chen, Pablo Carbajal, Edith Law, Laura Middleton, Kayla Regan, Mike Schaekermann, George Trimponias, James Tung, Pascal Poupart:
Online Bayesian Transfer Learning for Sequential Data Modeling. ICLR (Poster) 2017 - 2016
- [c2]Pascal Poupart, Zhitang Chen, Priyank Jaini, Fred Fung, Hengky Susanto, Yanhui Geng, Li Chen, Kai Chen, Hao Jin:
Online flow size prediction for improved network routing. ICNP 2016: 1-6 - [c1]Priyank Jaini, Abdullah Rashwan, Han Zhao, Yue Liu, Ershad Banijamali, Zhitang Chen, Pascal Poupart:
Online Algorithms for Sum-Product Networks with Continuous Variables. Probabilistic Graphical Models 2016: 228-239 - [i1]Priyank Jaini, Pascal Poupart:
Online and Distributed learning of Gaussian mixture models by Bayesian Moment Matching. CoRR abs/1609.05881 (2016)
Coauthor Index
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last updated on 2024-11-15 20:39 CET by the dblp team
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