Bayesian Neural Networks
Small-variance asymptotics for Bayesian neural networks
... to Bayesian neural ...standard neural networks and Bayesian neural networks, showing that the loss function for a standard neural network may be obtained in the ...
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Bayesian neural networks increasingly sparsify their units with depth
... for neural networks in ...on Bayesian neural networks by providing a characteriza- tion of the marginal prior distribution of the ...
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Understanding Priors in Bayesian Neural Networks at the Unit Level
... 5. Conclusion and future work Despite the ubiquity of deep learning throughout science, medicine and engineering, the underlying theory has not kept pace with applications for deep learning. In this paper, we have ...
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Gradient-Free Adversarial Attacks for Bayesian Neural Networks
... models. Bayesian neural networks (BNNs), due to their calibrated un- certainty, have been shown to posses favorable adversarial robustness ...approximate Bayesian inference methods are ...
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An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
... by Bayesian techniques em- ploying Markov chain Monte Carlo (MCMC) and other sampling techniques ...These Bayesian techniques yield a posterior dis- tribution which constrains the range of values that fit ...
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Bayesian neural networks become heavier-tailed with depth
... Introduction Bayesian approaches investigate models by assuming a prior distribution on their ...parameters. Bayesian machine learning refers to extending standard machine learning approaches with pos- ...
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Efficient Calculation of Adversarial Examples for Bayesian Neural Networks
... for Bayesian neural networks is ...attack Bayesian neural networks with adversar- ial examples calculated for a deterministic network with parameters given by the mean of the ...
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Robustness of Bayesian Neural Networks to Gradient-Based Attacks
... 7 Broader Impact This work is a theoretical investigation in the large data limit of vulnerability of Bayesian Neural Networks to gradient-based attacks. The main result is that, in this limit, BNNs ...
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Personalizing gesture recognition using hierarchical bayesian neural networks
... hierarchical Bayesian neural networks to capture subject-specific variations and share statistical strength across ...learning Bayesian neural networks, we build fast, scalable ...
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Model Selection in Bayesian Neural Networks via Horseshoe Priors
... effective model selection in Bayesian neural networks by placing horseshoe (Carvalho et al., 2009) and related priors over the variance of weights incident to each node in the network. These priors ...
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Bayesian neural networks for detecting epistasis in genetic association studies
... of Bayesian neural networks (BNNs) for association studies to directly ad- dress some the issues with current epistasis ...complex Bayesian models, BNNs require sto- chastic sampling ...
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Understanding the Genetic Etiology of Complex Phenotypes using Bayesian Neural Networks.
... Software implementing the approaches outlined in thesis is available for the Python program- ming language. The code base has grown from a few simple functions into a fully formed and very flexible software package. ...
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Evaluating the Robustness of Bayesian Neural Networks Against Different Types of Attacks
... of Bayesian neural net- works on image classification tasks, we perform input per- turbations, and adversarial attacks to the state-of-the-art Bayesian neural networks, with a benchmark ...
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Learning and policy search in stochastic dynamical systems with Bayesian neural networks
... We present an algorithm for policy search in stochastic dynamical systems using model-based reinforcement learning. The system dynamics are described with Bayesian neural networks (BNNs) that include ...
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Bayesian neural networks for sparse coding
... Sparse coding is widely used in different applications, such as compressive sensing [13], image and video processing [14, 15], neuroscience [16, 17]. A novel method to propagate uncertainty through the soft-thresholding ...
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Structured Dropout Variational Inference for Bayesian neural networks
... 1. maintain the backpropagation in parallel and optimize efficiently with gradient-based methods 2. acquire flexible Bayesian inference in terms of both prior and approximate posterior , but guarantee KL-condition ...
Microwave Characterization of Dielectric Materials Using Bayesian Neural Networks
... of neural networks (NN), coupled with the finite element method (FEM), to evaluate the broad- band properties of dielectric ...on Bayesian regularization ensures a good generalization capability of ...
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Introspective Robot Perception using Smoothed Predictions from Bayesian Neural Networks
... a Bayesian Neural Network (BNN), and evaluate two practical inference techniques to ob- tain better uncertainty estimates, namely Concrete Dropout (CDP) and Kronecker-factored Laplace Approximation ...
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Bayesian Artificial Neural Networks in Health and Cybersecurity
... with Bayesian neural net- works opens a new era of hope for modeling count data as well as for survival ...the Bayesian setting helps to identify the relative importance of the covariates in an ANN ...
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Bayesian methods for gravitational waves and neural networks
... feed-forward networks, where nodes are arranged in ordered ...a Bayesian optimisation algorithm that uses second-order in- formation on the log-posterior function in order to find the optimal set of weights ...
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