Zobrazeno 1 - 10
of 81
pro vyhledávání: '"Pakman, Ari"'
Autor:
Cohen, Yarden, Navarro, Alexandre Khae Wu, Frellsen, Jes, Turner, Richard E., Riemer, Raziel, Pakman, Ari
The need for regression models to predict circular values arises in many scientific fields. In this work we explore a family of expressive and interpretable distributions over circle-valued random functions related to Gaussian processes targeting two
Externí odkaz:
http://arxiv.org/abs/2406.13151
Autor:
Pakman, Ari
We show that Hamiltonian Monte Carlo, applied to the von Mises distribution with Laplace distribution for the momentum, has exactly solvable equations of motion. With an appropriate travel time, the Markov chain has negative autocorrelation at odd la
Externí odkaz:
http://arxiv.org/abs/2312.16546
Probability density models based on deep networks have achieved remarkable success in modeling complex high-dimensional datasets. However, unlike kernel density estimators, modern neural models do not yield marginals or conditionals in closed form, a
Externí odkaz:
http://arxiv.org/abs/2106.04741
Autor:
Pakman, Ari, Nejatbakhsh, Amin, Gilboa, Dar, Makkeh, Abdullah, Mazzucato, Luca, Wibral, Michael, Schneidman, Elad
Publikováno v:
NeurIPS 2021
The integration and transfer of information from multiple sources to multiple targets is a core motive of neural systems. The emerging field of partial information decomposition (PID) provides a novel information-theoretic lens into these mechanisms
Externí odkaz:
http://arxiv.org/abs/2102.00218
Autor:
Wang, Yueqi, Lee, Yoonho, Basu, Pallab, Lee, Juho, Teh, Yee Whye, Paninski, Liam, Pakman, Ari
Learning community structures in graphs has broad applications across scientific domains. While graph neural networks (GNNs) have been successful in encoding graph structures, existing GNN-based methods for community detection are limited by requirin
Externí odkaz:
http://arxiv.org/abs/2010.15727
Publikováno v:
Published in Proceedings of the 37th International Conference on Machine Learning, Vienna, Austria, PMLR 119, 2020
Probabilistic clustering models (or equivalently, mixture models) are basic building blocks in countless statistical models and involve latent random variables over discrete spaces. For these models, posterior inference methods can be inaccurate and/
Externí odkaz:
http://arxiv.org/abs/1901.00409
Autor:
Pakman, Ari, Paninski, Liam
We develop methods for efficient amortized approximate Bayesian inference over posterior distributions of probabilistic clustering models, such as Dirichlet process mixture models. The approach is based on mapping distributed, symmetry-invariant repr
Externí odkaz:
http://arxiv.org/abs/1811.09747
Autor:
Pakman, Ari
The Bouncy Particle Sampler is a novel rejection-free non-reversible sampler for differentiable probability distributions over continuous variables. We generalize the algorithm to piecewise differentiable distributions and apply it to generic binary
Externí odkaz:
http://arxiv.org/abs/1711.00922
We introduce a novel stochastic version of the non-reversible, rejection-free Bouncy Particle Sampler (BPS), a Markov process whose sample trajectories are piecewise linear. The algorithm is based on simulating first arrival times in a doubly stochas
Externí odkaz:
http://arxiv.org/abs/1609.00770