Scalable Matrix Factorization for Novel Inference in Exponential Families


Scalable Matrix Factorization for Novel Inference in Exponential Families – We present a neural-network-based method for the identification of high-dimensional and sparse binary patterns in probabilistic systems. Our framework combines a recurrent neural network (RNN) and a Gaussian process (GP). The recurrent encoder is a generative model which is adapted to learning from binary data. We also exploit this recurrent encoder to represent binary patterns using latent space models, which is a common form of binary data in probabilistic systems. Experiments on synthetic data demonstrate that the proposed deep learning approaches show better performance than state-of-the-art approaches.

The use of object-space models to deal with uncertainty and ambiguity arises in many applications, such as the task of human navigation. A new approach based on the notion of object-space model is proposed, which can be used to model uncertainty for any uncertainty associated with the object. The paradigm of object-space modeling is based on a two-stage approach, where the model is used to model uncertainty and a set of models are extracted from the two steps. The models are then combined in order to achieve a two-stage representation based on a joint posterior distribution, whereby the model is used to model uncertainty. The representation of uncertainty is derived from the number of examples represented in the training data. The proposed approach is tested for two-way interaction between two-dimensional (1,2) and three-dimensional (3,4,5) sets of objects. The experimental results show that our method consistently achieves superior performance compared to state-of-the-art approaches.

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Tangled Watermarks for Deep Neural Networks

Scalable Matrix Factorization for Novel Inference in Exponential Families

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  • Binary LSH Kernel and Kronecker-factored Transform for Stochastic Monomial Latent Variable Models

    Towards a unified paradigm of visual questions and information domain modelingThe use of object-space models to deal with uncertainty and ambiguity arises in many applications, such as the task of human navigation. A new approach based on the notion of object-space model is proposed, which can be used to model uncertainty for any uncertainty associated with the object. The paradigm of object-space modeling is based on a two-stage approach, where the model is used to model uncertainty and a set of models are extracted from the two steps. The models are then combined in order to achieve a two-stage representation based on a joint posterior distribution, whereby the model is used to model uncertainty. The representation of uncertainty is derived from the number of examples represented in the training data. The proposed approach is tested for two-way interaction between two-dimensional (1,2) and three-dimensional (3,4,5) sets of objects. The experimental results show that our method consistently achieves superior performance compared to state-of-the-art approaches.


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