Ari Pakman

According to our database, Ari Pakman authored at least 21 papers between 2013 and 2025.

Collaborative distances:

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

2025

Clustering via Self-Supervised Diffusion.

Roy Uziel, Irit Chelly, Oren Freifeld, Ari Pakman

CoRR, July, 2025

Super-efficient exact Hamiltonian Monte Carlo for the von Mises distribution.

Ari Pakman

Appl. Math. Lett., 2025

Bayesian Circular Regression with von Mises Quasi-Processes.

Yarden Cohen, Alexandre K. W. Navarro, Jes Frellsen, Richard E. Turner, Raziel Riemer, Ari Pakman

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

Consistent Amortized Clustering via Generative Flow Networks.

Irit Chelly, Roy Uziel, Oren Freifeld, Ari Pakman

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

von Mises Quasi-Processes for Bayesian Circular Regression.

Yarden Cohen, Alexandre K. W. Navarro, Jes Frellsen, Richard E. Turner, Raziel Riemer, Ari Pakman

CoRR, 2024

2021

Marginalizable Density Models.

Dar Gilboa, Ari Pakman, Thibault Vatter

CoRR, 2021

Estimating the Unique Information of Continuous Variables.

Ari Pakman, Dar Gilboa, Elad Schneidman

CoRR, 2021

Estimating the Unique Information of Continuous Variables.

Ari Pakman, Amin Nejatbakhsh, Dar Gilboa, Abdullah Makkeh, Luca Mazzucato, Michael Wibral, Elad Schneidman

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

2020

Attentive Clustering Processes.

Ari Pakman, Yueqi Wang, Yoonho Lee, Pallab Basu, Juho Lee, Yee Whye Teh, Liam Paninski

CoRR, 2020

Neural Clustering Processes.

Ari Pakman, Yueqi Wang, Catalin Mitelut, Jin Hyung Lee, Liam Paninski

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

2019

Discrete Neural Processes.

Ari Pakman, Liam Paninski

CoRR, 2019

Neural Permutation Processes.

Ari Pakman, Yueqi Wang, Liam Paninski

Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2019

2018

Amortized Bayesian inference for clustering models.

Ari Pakman, Liam Paninski

CoRR, 2018

2017

Stochastic Bouncy Particle Sampler.

Ari Pakman, Dar Gilboa, David E. Carlson, Liam Paninski

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

Taming the Noise in Reinforcement Learning via Soft Updates.

Roy Fox, Ari Pakman, Naftali Tishby

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Partition Functions from Rao-Blackwellized Tempered Sampling.

David E. Carlson, Patrick Stinson, Ari Pakman, Liam Paninski

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

G-Learning: Taming the Noise in Reinforcement Learning via Soft Updates.

Roy Fox, Ari Pakman, Naftali Tishby

CoRR, 2015

2014

Fast state-space methods for inferring dendritic synaptic connectivity.

Ari Pakman, Jonathan Hunter Huggins, Carl Smith, Liam Paninski

J. Comput. Neurosci., 2014

2013

Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits.

Benjamin Shababo, Brooks Paige, Ari Pakman, Liam Paninski

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions.

Ari Pakman, Liam Paninski

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Bayesian spike inference from calcium imaging data.

Eftychios A. Pnevmatikakis, Josh Merel, Ari Pakman, Liam Paninski

Proceedings of the 2013 Asilomar Conference on Signals, 2013