Results 11 to 20 of about 11,193 (260)
Gauging Variational Inference [PDF]
Journal of Statistical Mechanics: Theory and Experiment, 2017 Abstract Computing of partition function is the most important statistical inference task arising in applications of graphical models (GM). Since it is computationally intractable, approximate methods have been used in practice, where mean-field (MF) and belief propagation (BP) are arguably the most popular and successful approaches ...
Sungsoo Ahn +2 more
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Evaluating probabilistic programming and fast variational Bayesian inference in phylogenetics [PDF]
PeerJ, 2019 Recent advances in statistical machine learning techniques have led to the creation of probabilistic programming frameworks. These frameworks enable probabilistic models to be rapidly prototyped and fit to data using scalable approximation methods such ...
Mathieu Fourment, Aaron E. Darling
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Sliced Wasserstein Variational Inference [PDF]
CoRR, 2022 Variational Inference approximates an unnormalized distribution via the minimization of Kullback-Leibler (KL) divergence. Although this divergence is efficient for computation and has been widely used in applications, it suffers from some unreasonable properties.
Yi, Mingxuan, Liu, Song
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CoRR, 2022 Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e.g. gene expressions, impulse responses, human faces) and covariates are relatively limited.
Wu, Yulun +5 more
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IEEE Access, 2023 This paper proposes a model estimation method in offline Bayesian model-based reinforcement learning (MBRL). Learning a Bayes-adaptive Markov decision process (BAMDP) model using standard variational inference often suffers from poor predictive ...
Toru Hishinuma, Kei Senda
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Variational Inference with a Quantum Computer [PDF]
Physical Review Applied, 2021 17 pages, 9 figures, 1 table; As published in Phys.
Marcello Benedetti +4 more
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A Novel Anti-Jamming Technique for INS/GNSS Integration Based on Black Box Variational Inference
Applied Sciences, 2021 In this paper, a novel anti-jamming technique based on black box variational inference for INS/GNSS integration with time-varying measurement noise covariance matrices is presented. We proved that the time-varying measurement noise is more similar to the
Ping Dong, Jianhua Cheng, Liqiang Liu
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A Design Methodology for Fault-Tolerant Neuromorphic Computing Using Bayesian Neural Network
Micromachines, 2023 Memristor crossbar arrays are a promising platform for neuromorphic computing. In practical scenarios, the synapse weights represented by the memristors for the underlying system are subject to process variations, in which the programmed weight when read
Di Gao, Xiaoru Xie, Dongxu Wei
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GFlowNets and variational inference
CoRR, 2022 This paper builds bridges between two families of probabilistic algorithms: (hierarchical) variational inference (VI), which is typically used to model distributions over continuous spaces, and generative flow networks (GFlowNets), which have been used for distributions over discrete structures such as graphs.
Nikolay Malkin +7 more
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Collaborative filtering recommendation algorithm based on variational inference
International Journal of Crowd Science, 2020 PurposeThe purpose of this paper is to alleviate the problem of poor robustness and over-fitting caused by large-scale data in collaborative filtering recommendation algorithms.Design/methodology/approachInterpreting user behavior from the probabilistic ...
Kai Zheng +4 more
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