Paper presents a brain tumor segmentation method using NGMM and 3D FVF.
problem Automatic brain tumor segmentation from MRIs is challenging.
method Normalized Gaussian Bayesian classifier and 3D Fluid Vector Flow algorithm.
result The method successfully segments brain tumors from MRI images.
DeepCCG adapts classifiers to representation shifts in one step.
problem Adapting classifiers to shifts in continuous representation.
method Empirical Bayesian approach using class conditional Gaussian classifier and KL divergence for selection.
result DeepCCG reduces performance change due to representation shifts.
A new metric estimates classifier accuracy using only training data.
problem Assessing classifier accuracy without cross-validation.
method Bayesian Area Under the ROC Curve (CBAUC) metric for linear classifiers.
result The CBAUC is faster and more accurate than conventional AUC estimators.
When modeling a probability distribution with a Bayesian network, we are faced with the problem of how to handle continuous variables. Most previous work has either solved the problem by discretizing, or assumed that the data are generated by a single Gaussian. In this paper we abandon the normality assumption and inst…
Classifiers based on probabilistic graphical models are very effective. In continuous domains, maximum likelihood is usually used to assess the predictions of those classifiers. When data is scarce, this can easily lead to overfitting. In any probabilistic setting, Bayesian averaging (BA) provides theoretically optimal…
Bayesian method improves few-shot classification accuracy.
problem Few-shot classification with small labeled datasets.
method Gaussian process classifier with Pólya-Gamma augmentation and one-vs-each softmax.
result Improved accuracy and uncertainty quantification.
New model uses heteroscedastic Gaussian process for alkenone SST proxy.
problem Restoring historical sea surface temperatures using proxies.
method Heteroscedastic Gaussian process regression method.
result Nonparametric approach handles variable noise patterns and outliers.
A new method reduces energy consumption in machine learning by using multiple, less costly data sources.
problem High computational and energy costs in machine learning model training.
method Augmented Gaussian Process (AGP-MISO) with multi-source optimization.
result The AGP-MISO method reduces computational time and energy consumption compared to traditional approaches.
This report works out the details of a closed-form, fully Bayesian, multiclass, openset, generative pattern classifier using multivariate Gaussian likelihoods, with conjugate priors. The generative model has a common within-class covariance, which is proportional to the between-class covariance in the conjugate prior. …
Skew Gaussian Processes improve classification performance by allowing asymmetry.
problem Limited use of Gaussian processes in applications requiring asymmetry.
method Propose Skew-Gaussian processes (SkewGPs) as a non-parametric prior over functions, extending the multivariate Unified Skew-Normal distribution to stochastic processes.
result SkewGPs provide better performance than symmetric Gaussian processes in classification tasks.
Proposes a sparse classifier for discriminative Gaussian Mixture Models.
problem Softmax-based discriminative models assume unimodality, leading to parameter redundancy.
method Sparse Bayesian learning for GMM-based discriminative model, reducing parameters and complexity.
result The SDGM outperforms existing softmax-based discriminative models.
In this study, we present a multi-class graphical Bayesian predictive classifier that incorporates the uncertainty in the model selection into the standard Bayesian formalism. For each class, the dependence structure underlying the observed features is represented by a set of decomposable Gaussian graphical models. Emp…
Bayesian method classifies actin cytoskeleton networks using topological data.
problem Classifying the structure of biological networks, especially actin cytoskeleton networks.
method Transform actin cytoskeleton networks into persistence diagrams, quantify variability with Bayesian framework, estimate posterior distributions.
result Bayesian framework successfully classifies actin filament networks, outperforming state-of-the-art methods.
Bayesian quadrature uses probabilistic models for estimating intractable integrals.
problem Estimating intractable integrals in complex models.
method Probabilistic, model-based approach using Gaussian processes.
result Comprehensive review and systematic taxonomy of Bayesian quadrature methods.
Bayesian approach for aggregating unreliable data sources to create accurate heatmaps.
problem Classifying regions with sparse, unreliable data from multiple sources.
method Bayesian Gaussian Process classifier that models reliability and bias of each data source.
result Reduces crowdsourced data needed and improves accuracy of heatmaps.
Bayesian nonparametric method segments multi-sequence time series data.
problem Temporal segmentation of multi-sequence time series data into stationary segments.
method Gaussian process priors and nonparametric distribution for segment partitioning.
result Model effectively segments synthetic and real-time series data.
The main challenges that arise when adopting Gaussian Process priors in probabilistic modeling are how to carry out exact Bayesian inference and how to account for uncertainty on model parameters when making model-based predictions on out-of-sample data. Using probit regression as an illustrative working example, this …
The most important aspect of any classifier is its error rate, because this quantifies its predictive capacity. Thus, the accuracy of error estimation is critical. Error estimation is problematic in small-sample classifier design because the error must be estimated using the same data from which the classifier has been…
GPry accelerates Bayesian inference for non-Gaussian posteriors.
problem Efficient Bayesian inference for complex models with moderate parameters.
method Generative Gaussian Process surrogate model with SVM classifier, active learning.
result Significant reduction in inference time and computational cost.
The paper classifies U.S. crop types using hyperspectral satellite imagery.
problem Classifying crop types from hyperspectral satellite imagery.
method Gaussian Bayesian models and neural networks applied to NASA data.
result Bayesian methods outperform standard LDA and QDA.
BOE reformulates BO as a classifier for scalable batch optimisation.
problem Scalable batch optimisation of expensive functions.
method Reformulates BO as density-ratio estimation, removing need for explicit function prior.
result Theoretical guarantees and improved uncertainty estimates for batch optimisation.
In decision-making systems, it is important to have classifiers that have calibrated uncertainties, with an optimisation objective that can be used for automated model selection and training. Gaussian processes (GPs) provide uncertainty estimates and a marginal likelihood objective, but their weak inductive biases lead…
DS-UI improves DNN uncertainty inference by combining a DNN classifier with MoGMM.
problem Improving uncertainty inference in DNN-based image recognition.
method Combines DNN classifier with MoGMM for probabilistic interpretation of features.
result DS-UI outperforms state-of-the-art UI methods in misclassification detection.
FABLE incorporates instance features into PWS label models for improved performance.
problem Lack of instance features in existing label models limits their performance.
method FABLE uses a mixture of Bayesian label models and a Gaussian Process classifier to incorporate instance features.
result FABLE achieves the highest averaged performance across nine baselines on benchmark datasets.
We consider the problem of discriminative factor analysis for data that are in general non-Gaussian. A Bayesian model based on the ranks of the data is proposed. We first introduce a new {\em max-margin} version of the rank-likelihood. A discriminative factor model is then developed, integrating the max-margin rank-lik…
The paper introduces new Bayesian network classifiers for better classification accuracy.
problem Improving supervised classification accuracy using Bayesian network classifiers.
method Developed novel classes of generative classifiers based on staged tree models, extending Bayesian networks.
result Data-driven learning routines enhance the accuracy of the new classifiers.
Bayesian optimization method predicts high costs for unstable robot controllers.
problem Time-consuming and challenging learning robot controllers with unknown penalties.
method Proposes a Bayesian model that predicts high costs in unstable regions.
result Improves robot learning by guiding exploration toward stable regions.
New classifier uses lower fidelity models to speed up expensive simulations.
problem Expensive computational models limit machine learning applications.
method Autoregressive model with Gaussian process priors, active learning, and sparse approximation.
result Median computational cost reduction of 23% for target accuracy of 90%.
The paper uses Gaussian mixture models for Bayesian networks and proposes an optimization algorithm.
problem Modeling nodes in Bayesian networks with complex distributions.
method Gaussian mixture models combined with double iteration algorithm.
result The double iteration algorithm optimizes Gaussian mixture models effectively.
Bayesian Optimization uses Deep Gaussian Processes for non-stationary functions.
problem Optimizing expensive non-stationary functions with classic Gaussian Processes.
method Deep Gaussian Processes as surrogate models for capturing non-stationarity.
result The proposed algorithm outperforms state-of-the-art methods on analytical and aerospace design problems.
Bayesian inverse problems solved with Gaussian models for PDEs.
problem Solving inverse problems with limited data for PDEs.
method Constructing PDE-informed Gaussian priors for Bayesian inversion.
result PDE-informed Gaussian priors outperform traditional priors.
A new method combines Gaussian Processes to optimize under uncertainty.
problem Bayesian Optimization's weakness in fitting Gaussian Processes.
method Wasserstein Barycenter Gaussian Process (WBGP) approach.
result WBGP-BO converges to the optimum, improving on vanilla BO.
Brain-Computer Interface (BCI) uses brain signals in order to provide a new method for communication between human and outside world. Feature extraction, selection and classification are among the main matters of concerns in signal processing stage of BCI. In this article, we present our findings about the most effecti…
Bayesian optimization sped up with scalable Gaussian processes.
problem Optimizing functions with derivative information and large datasets.
method Combines derivative acceleration and scalable Gaussian process models.
result Significant speedup in optimization convergence for large datasets.
VMGP extends Gaussian processes for Bayesian meta-learning, improving uncertainty prediction.
problem Bayesian meta-learning for few-shot tasks with non-Gaussian uncertainty.
method VMGP (Variational Meta-Gaussian Processes) extends Gaussian processes to model non-Gaussian predictive posteriors.
result VMGP significantly outperforms existing Bayesian meta-learning methods on complex tasks.
Characterizes uncertainty in high-dimensional linear classification models.
problem Assessing uncertainty in high-dimensional linear classification models.
method Approximate message passing algorithm for posterior marginals, closed-form formula for joint statistics.
result Closed-form formula for joint statistics between logistic classifier, Bayesian uncertainty, and ground-truth probit uncertainty.
We consider the task of one-shot learning of visual categories. In this paper we explore a Bayesian procedure for updating a pretrained convnet to classify a novel image category for which data is limited. We decompose this convnet into a fixed feature extractor and softmax classifier. We assume that the target weights…
Optimizes Gaussian process hyperparameters using Bayesian autoregression.
problem Optimizing hyperparameters for Matérn kernel temporal Gaussian processes.
method Recursive Bayesian estimation for autoregressive parameters.
result Outperforms traditional optimization methods in runtime and accuracy.
New method to assess uncertainty in Bayesian optimization.
problem Uncertainty quantification in Bayesian optimization.
method Constructing confidence regions of the maximum point or value of the objective function.
result Unified uncertainty quantification framework for various sampling policies and stopping criteria.
We propose a Bayesian framework of Gaussian process in order to extend Fisher's discriminant to classify functional data such as spectra and images. The probability structure for our extended Fisher's discriminant is explicitly formulated, and we utilize the smoothness assumptions of functional data as prior probabilit…
Bayesian inference for wide neural networks using Edgeworth expansion.
problem Analyzing the non-Gaussian behavior of wide neural networks in Bayesian inference.
method Proposed a non-Gaussian distribution using multivariate Edgeworth expansion for finite-width neural networks.
result Derived non-Gaussian posterior distribution in Bayesian regression tasks.
New method optimizes processes under constraints using bivariate Gaussian models.
problem Optimizing processes with constraints using traditional methods.
method Developed a constrained expected improvement acquisition function using bivariate Gaussian process models.
result Demonstrated improved performance in a manufacturing cure process optimization.
Proposes a Bayesian Autoencoder with sparse Gaussian process priors to capture data correlations.
problem Autoencoders' i.i.d. assumption of latent representations fails to capture data correlations.
method Imposes fully Bayesian sparse Gaussian Process priors on the latent space of a Bayesian Autoencoder and uses stochastic gradient Hamiltonian Monte Carlo for posterior estimation.
result Consistently outperforms alternatives relying on Variational Autoencoders on various tasks.
This paper explores approximations for fully Bayesian Gaussian Process Regression.
problem Learning in Gaussian Process models through hyperparameter adaptation.
method Two approximation schemes: Hamiltonian Monte Carlo and Variational Inference.
result Predictive performance analysis on various benchmark datasets.
Proposes TAGI for efficient Gaussian inference in Bayesian neural networks.
problem Efficient inference in Bayesian neural networks with complex architectures.
method Analytical method for tractable approximate Gaussian inference (TAGI).
result Matches performance of gradient-based methods with O(n) computational complexity. Improved Bayesian optimisation method using randomised Gaussian process UCB.
problem Improving performance in Bayesian optimisation.
method Developed a modified Gaussian process upper confidence bound (GP-UCB) acquisition function.
result The method achieves better performance than GP-UCB in various problems.
Bayesian model improves BCI performance for ALS users.
problem Classifying EEG signals for P300 BCIs with low SNR and complex correlations.
method GLASS model with Gaussian Latent channel and Sparse time-varying effects.
result GLASS substantially improves BCI performance in ALS users.
Decentralized Gaussian processes for multi-agent systems.
problem Scalable and flexible learning solutions for multi-agent systems.
method Asymptotically exact decentralized solution to Gaussian processes, with online Bayesian model averaging for hyperparameter selection.
result Asymptotically exact decentralized Gaussian process approximation and online Bayesian model averaging.