Learning the Parameters of Linear Surfaces with Gaussian Processes


Learning the Parameters of Linear Surfaces with Gaussian Processes – This paper presents experimental results on a new type of nonconvex minimization problem. For the first time, the paper presents a nonconvex minimization algorithm that is based on the stochastic gradient descent algorithm. It is shown that the optimal solution at any position in the manifold is determined by the solution of a nonconvex linear equation. In this way, this minimization problem is solved using the stochastic gradient algorithm, which is the standard stochastic gradient descent algorithm. The paper first proposes a new nonconvex minimization algorithm which is the best of the two alternatives. The paper then goes on to present a first experimental result of the algorithm. We compare the proposed algorithm with several other minimization algorithms that are based on stochastic gradient descent and we compare its performance to other minimization algorithms. The empirical results demonstrate that the proposed algorithm is quite efficient.

Deep generative models and object models are becoming increasingly popular for representing, modelling and learning new data. In this paper, we propose a novel approach for learning generic objects that do not involve the appearance of human faces. This approach consists in a hierarchical generative model. The model is trained using an unsupervised learning method, which relies on pre-trained models for the tasks in hand and then combines the results of three pre-trained generative models. The learned objects represent both the human faces and the faces of others. We show that the approach can learn object models by using a novel spatial-temporal connection that is based on latent-source representations such as the appearance of human faces in real world images. The experiments on real-world datasets demonstrate that the technique can significantly outperform unsupervised supervised classification methods on both real-world and synthetic datasets.

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Learning the Parameters of Linear Surfaces with Gaussian Processes

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  • On-the-fly LSTM for Action Recognition on Video via a Spatio-Temporal Algorithm

    Multiagent Learning with Spatiotemporal Context: Application to Predicting Consumer’s BehaviorsDeep generative models and object models are becoming increasingly popular for representing, modelling and learning new data. In this paper, we propose a novel approach for learning generic objects that do not involve the appearance of human faces. This approach consists in a hierarchical generative model. The model is trained using an unsupervised learning method, which relies on pre-trained models for the tasks in hand and then combines the results of three pre-trained generative models. The learned objects represent both the human faces and the faces of others. We show that the approach can learn object models by using a novel spatial-temporal connection that is based on latent-source representations such as the appearance of human faces in real world images. The experiments on real-world datasets demonstrate that the technique can significantly outperform unsupervised supervised classification methods on both real-world and synthetic datasets.


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