Multiview Variational Sparse Gaussian Processes

Neural Operator Variational Inference Based on Regularized Stein Discrepancy for Deep Gaussian Processes

Deep Gaussian process (DGP) models offer a powerful nonparametric approach for Bayesian inference, but exact inference is typically intractable, motivating the use of various approximations. However, existing approaches, such as mean-field Gaussian assumptions, limit the expressiveness and efficacy of DGP models, while stochastic approximation can be computationally expensive. To tackle these challenges, we introduce neural operator variational inference (NOVI) for DGPs. NOVI uses a neural generator to obtain a sampler and minimizes the regularized Stein discrepancy (RSD) between the generated distribution and true posterior in $\mathcal {L}_{2}$ space. We solve the minimax problem using Monte Carlo estimation and subsampling stochastic optimization techniques and demonstrate that the bias introduced by our method can be controlled by multiplying the Fisher divergence with a constant, which leads to robust error control and ensures the stability and precision of the algorithm. Our experiments on datasets ranging from hundreds to millions demonstrate the effectiveness and the faster convergence rate of the proposed method. We achieve a classification accuracy of 93.56 on the CIFAR10 dataset, outperforming state-of-the-art (SOTA) Gaussian process (GP) methods. We are optimistic that NOVI possesses the potential to enhance the performance of deep Bayesian nonparametric models and could have significant implications for various practical applications.

Multi-View Deep Gaussian Processes for Supervised Learning

Multi-view learning is a widely studied topic in machine learning, which considers learning with multiple views of samples to improve the prediction performance. Even though some approaches have sprung up recently, it is still challenging to jointly explore information contained in different views. Multi-view deep Gaussian processes have shown strong advantages in unsupervised representation learning. However, they are limited when dealing with labeled multi-view data for supervised learning, and ignore the application potential of uncertainty estimation. In this paper, we propose a supervised multi-view deep Gaussian process model (named SupMvDGP), which uses the label of the views to further improve the performance, and takes the quantitative uncertainty estimation as a supplement to assist humans to make better use of prediction. According to the diversity of views, the SupMvDGP can establish asymmetric depth structure to better model different views, so as to make full use of the property of each view. We provide a variational inference method to effectively solve the complex model. Finally, we conduct comprehensive comparative experiments on multiple real world datasets to evaluate the performance of SupMvDGP. The experimental results show that the SupMvDGP achieves the state-of-the-art results in multiple tasks, which verifies the effectiveness and superiority of the proposed approach. Meanwhile, we provide a case study to show that the SupMvDGP has the ability to provide uncertainty estimation than alternative deep models, which can alert people to better treat the prediction results in high-risk applications.

Deep learning on multi-view sequential data: a survey

With the progress of human daily interaction activities and the development of industrial society, a large amount of media data and sensor data become accessible. Humans collect these multi-source data in chronological order, called multi-view sequential data (MvSD). MvSD has numerous potential application domains, including intelligent transportation, climate science, health care, public safety and multimedia, etc. However, as the volume and scale of MvSD increases, the traditional machine learning methods become difficult to withstand such large-scale data, and it is no longer appropriate to use hand-craft features to represent these complex data. In addition, there is no general framework in the process of mining multi-view relationships and integrating multi-view information. In this paper, We first introduce four common data types that constitute MvSD, including point data, sequence data, graph data, and raster data. Then, we summarize the technical challenges of MvSD. Subsequently, we review the recent progress in deep learning technology applied to MvSD. Meanwhile, we discuss how the network represents and learns features of MvSD. Finally, we summarize the applications of MvSD in different domains and give potential research directions.