Deep-MNIST: Recurrent Neural Network based Support Vector Learning


Deep-MNIST: Recurrent Neural Network based Support Vector Learning – We present Multi-layer Convolutional Neural Networks (ML-CNN). We generalize CNNs and ML-CNNs with multiple layers to two different models: deep-layer and deep-layer, respectively. The two models, however, are different in many important respects. One concerns the amount of training data: ML-CNNs usually learn the entire network architecture simultaneously, while deep-layer networks can only adapt one-layer, rather than multiple layers. We present a multi-layer ML-CNN architecture to train an ML-CNN, which jointly combines multiple layers in order to improve performance of each layer. We demonstrate both models on real datasets, on CIFAR-10, and on MNIST. Finally, we demonstrate the effectiveness of our ML-CNN approach on the CIFAR-10 dataset.

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.

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Deep-MNIST: Recurrent Neural Network based Support Vector Learning

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  • Learning to Distill Fine-Grained Context from Context-Aware Features

    Learning to Map Computations: The Case of Deep Generative ModelsRecent 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.


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