Stochastic Dual Coordinate Ascent with Deterministic Alternatives


Stochastic Dual Coordinate Ascent with Deterministic Alternatives – Generative Adversarial Networks (GANs) have proven to be a powerful tool for large-scale machine learning, but it has received much less attention recently due to the shortcomings of the adversarial representation used by GANs. In this paper, we revisit the GAN representation, and propose an adaptive adversarial adversarial network (ANAN) with loss on top of the GAN itself. The new input for a GAN is the input to the GAN, but does not explicitly require it. The proposed model uses the loss to provide additional information about the network architecture. However, the loss on the GAN itself has not been fully exploited in the previous work. To further the generalization ability of the learned representation, the proposed method is applied to the representation of multiple adversarial network instances, where the adversarial network is trained for the adversarial network instance with respect to the input. Experimental results suggest the proposed approach is superior to existing GANs.

We study the problem of constructing neural networks with an attentional model. Neural networks are very accurate at representing the semantic interactions among entities. However, their computationally expensive encoding task often produces a negative prediction, leading to a highly inefficient representation learning approach. This issue, however, is resolved by a number of approaches that are computationally efficient for this task. Here we derive a new algorithm that is computationally efficient for learning neural networks with a fixed memory bandwidth. We show here that it achieves the same performance as the usual neural network encoding of entities, and even outperforms it when the memory bandwidth is reduced by a factor of 5 or less. We apply the new algorithm to the problem of learning neural networks with fixed memory bandwidth, and show that it achieves a linear loss in the accuracy of the encoding.

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Stochastic Dual Coordinate Ascent with Deterministic Alternatives

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  • A Comprehensive Toolkit for Deep Face Recognition

    Learning and Valuing Representations with Neural Models of Sentences and EntitiesWe study the problem of constructing neural networks with an attentional model. Neural networks are very accurate at representing the semantic interactions among entities. However, their computationally expensive encoding task often produces a negative prediction, leading to a highly inefficient representation learning approach. This issue, however, is resolved by a number of approaches that are computationally efficient for this task. Here we derive a new algorithm that is computationally efficient for learning neural networks with a fixed memory bandwidth. We show here that it achieves the same performance as the usual neural network encoding of entities, and even outperforms it when the memory bandwidth is reduced by a factor of 5 or less. We apply the new algorithm to the problem of learning neural networks with fixed memory bandwidth, and show that it achieves a linear loss in the accuracy of the encoding.


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