Training Batch Faster with Convex Relaxations and Nonconvex Losses


Training Batch Faster with Convex Relaxations and Nonconvex Losses – We propose a novel, theoretically principled characterization of stochastic nonconvex loss. This characterization is based on a simple generalization of the maximum entropy loss, called the max-margin loss, and we show that the loss can be efficiently exploited in the stochastic setting, thus improving prediction performance. On the face of stochastic loss, our method obtains the least worst nonconvex loss in the stochastic setting, and hence outperforms the previous least worst stochastic loss, by an integer of its dimension (8-dimensional). Furthermore, as a result of our method, this loss can be computed in a simpler and more computationally efficient manner. We show the effectiveness of our method in challenging nonconvex loss conditions, with an empirical performance of 0.9%.

Despite the rapid progress in deep learning, the majority of recent deep learning models perform poorly in real-world applications, due to its prohibitive computational costs. In this paper, we propose a new approach to learn the state of deep convolutional neural networks. In deep learning, we first learn a representation of the state and predict potential future states from data. We then predict future states, that is, predict future states in the learned representation, with regret guarantees and leverage to improve prediction accuracy. We then train deep networks to predict future state representations. Our approach leverages a deep convolutional network architecture built on recurrent neural networks to predict future states. Our model outperforms a state network by 1.7 to 10.6 times accuracy when compared to a state network trained with only 3.2% prediction error. We show that our approach can lead to promising performance in real-world datasets.

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Training Batch Faster with Convex Relaxations and Nonconvex Losses

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    Learning to Recognize Raindrop Acceleration by Predicting SnowfallDespite the rapid progress in deep learning, the majority of recent deep learning models perform poorly in real-world applications, due to its prohibitive computational costs. In this paper, we propose a new approach to learn the state of deep convolutional neural networks. In deep learning, we first learn a representation of the state and predict potential future states from data. We then predict future states, that is, predict future states in the learned representation, with regret guarantees and leverage to improve prediction accuracy. We then train deep networks to predict future state representations. Our approach leverages a deep convolutional network architecture built on recurrent neural networks to predict future states. Our model outperforms a state network by 1.7 to 10.6 times accuracy when compared to a state network trained with only 3.2% prediction error. We show that our approach can lead to promising performance in real-world datasets.


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