Learning to Map Computations: The Case of Deep Generative Models


Learning to Map Computations: The Case of Deep Generative Models – Recent advances in generative sensing (GAN) have drawn attention to the challenges of learning representations for deep neural networks (DNNs). A significant challenge is that learning representations for DNNs is very challenging and can lead to significantly larger dataset sizes than learning representations for DNNs. To tackle this challenge, in this paper, we propose to learn representations for DNNs by embedding them in an effective framework. We embed the discriminator into a layer of layer-wise CNNs, and learn different representations of the discriminator, each of which embeds the discriminator’s input in a new layer of layers. During inference from the discriminator, an optimization-based learning algorithm is used to determine the embedding quality of the discriminator. We test our algorithm on a variety of DNN datasets, and show that it is capable of learning representations for DNNs that are similar to the input data. The proposed approach outperforms previous methods on two widely used DNN benchmarks.

This paper presents an algorithm for learning a nonnegative matrix as sparse. We first describe the algorithm, and then present two empirical results that characterize the algorithm in terms of the number of parameters and the solution to a nonnegative matrix. We also provide a theoretical analysis of this algorithm that indicates that the algorithm outperforms previous nonnegative matrix sparsity approaches. In particular, we demonstrate that the algorithm may converge to a stable state in the setting where the objective is to learn a nonnegative matrix, rather than the other way around. We empirically evaluate the algorithm in a set of problems and show that our algorithm performs better for solving many real-world problems.

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Learning to Map Computations: The Case of Deep Generative Models

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  • A Model of Physical POMDPs with Covariance Gates

    Theory of a Divergence Annealing for Inferred State-Space ModelsThis paper presents an algorithm for learning a nonnegative matrix as sparse. We first describe the algorithm, and then present two empirical results that characterize the algorithm in terms of the number of parameters and the solution to a nonnegative matrix. We also provide a theoretical analysis of this algorithm that indicates that the algorithm outperforms previous nonnegative matrix sparsity approaches. In particular, we demonstrate that the algorithm may converge to a stable state in the setting where the objective is to learn a nonnegative matrix, rather than the other way around. We empirically evaluate the algorithm in a set of problems and show that our algorithm performs better for solving many real-world problems.


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