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Found 11 papers, 3 papers with code
A Bayesian sparse factor model with adaptive posterior concentration
In this paper, we propose a new Bayesian inference method for a high-dimensional sparse factor model that allows both the factor dimensionality and the sparse structure of the loading matrix to be inferred.
Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference
Bayesian approaches for learning deep neural networks (BNN) have been received much attention and successfully applied to various applications.
Intrinsic and extrinsic deep learning on manifolds
We propose extrinsic and intrinsic deep neural network architectures as general frameworks for deep learning on manifolds.
The convergent Indian buffet process
We propose a new Bayesian nonparametric prior for latent feature models, which we call the convergent Indian buffet process (CIBP).
Masked Bayesian Neural Networks : Computation and Optimality
As data size and computing power increase, the architectures of deep neural networks (DNNs) have been getting more complex and huge, and thus there is a growing need to simplify such complex and huge DNNs.
SLIDE: a surrogate fairness constraint to ensure fairness consistency
As they have a vital effect on social decision makings, AI algorithms should be not only accurate and but also fair.
Learning fair representation with a parametric integral probability metric
That is, we derive theoretical relations between the fairness of representation and the fairness of the prediction model built on the top of the representation (i. e., using the representation as the input).
Adaptive variational Bayes: Optimality, computation and applications
It turns out that this combined variational posterior is the closest member to the posterior over the entire model in a predefined family of approximating distributions.
Nonconvex sparse regularization for deep neural networks and its optimality
Recent theoretical studies proved that deep neural network (DNN) estimators obtained by minimizing empirical risk with a certain sparsity constraint can attain optimal convergence rates for regression and classification problems.
Smooth function approximation by deep neural networks with general activation functions
Based on our approximation error analysis, we derive the minimax optimality of the deep neural network estimators with the general activation functions in both regression and classification problems.
Fast convergence rates of deep neural networks for classification
In addition, we consider a DNN classifier learned by minimizing the cross-entropy, and show that the DNN classifier achieves a fast convergence rate under the condition that the conditional class probabilities of most data are sufficiently close to either 1 or zero.
