A Noise-Robust Fast Sparse Bayesian Learning Model [PDF]
, 2020 This paper utilizes the hierarchical model structure from the Bayesian Lasso in the Sparse Bayesian Learning process to develop a new type of probabilistic supervised learning approach.
Helgøy, Ingvild M., Li, Yushu
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Risk, Unexpected Uncertainty, and Estimation Uncertainty: Bayesian Learning in Unstable Settings [PDF]
, 2011 Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty.
A Quinn +49 more
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Second-Order Belief Hidden Markov Models [PDF]
, 2014 Hidden Markov Models (HMMs) are learning methods for pattern recognition. The probabilistic HMMs have been one of the most used techniques based on the Bayesian model.
A. Aregui +17 more
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Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa
, 2018 This paper presents a generic Bayesian framework that enables any deep learning model to actively learn from targeted crowds. Our framework inherits from recent advances in Bayesian deep learning, and extends existing work by considering the targeted ...
Damianou, Andreas +3 more
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A nonparametric Bayesian approach toward robot learning by demonstration
, 2012 In the past years, many authors have considered application of machine learning methodologies to effect robot learning by demonstration. Gaussian mixture regression (GMR) is one of the most successful methodologies used for this purpose.
Antoniak +35 more
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BRUNO: A Deep Recurrent Model for Exchangeable Data [PDF]
, 2018 We present a novel model architecture which leverages deep learning tools to perform exact Bayesian inference on sets of high dimensional, complex observations.
Dambre, Joni +5 more
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Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI
, 2014 We develop a Bayesian nonparametric model for reconstructing magnetic resonance images (MRI) from highly undersampled k-space data. We perform dictionary learning as part of the image reconstruction process.
Ding, Xinghao +5 more
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????????? ?????? ???????????? ?????? ???????????? ?????? ??????????????? ???????????? ????????? ???????????? ?????? [PDF]
, 2020 Department of Computer Science and EngineeringAs deep learning has grown fast, so did the desire to interpret deep learning black boxes. As a result, many analysis tools have emerged to interpret it.
Lee, Ginkyeng
core
High-Order Stochastic Gradient Thermostats for Bayesian Learning of Deep Models
, 2015 Learning in deep models using Bayesian methods has generated significant attention recently. This is largely because of the feasibility of modern Bayesian methods to yield scalable learning and inference, while maintaining a measure of uncertainty in the
Carin, Lawrence +3 more
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Bayesian Semi-supervised Learning with Graph Gaussian Processes [PDF]
, 2018 We propose a data-efficient Gaussian process-based Bayesian approach to the semi-supervised learning problem on graphs. The proposed model shows extremely competitive performance when compared to the state-of-the-art graph neural networks on semi ...
Colombo, Nicolo +2 more
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