Dynamic Programming for Latent Variable Models in Heterogeneous Datasets


Dynamic Programming for Latent Variable Models in Heterogeneous Datasets – We propose a new framework for probabilistic inference from discrete data. This requires the assumption that the data are stable (i.e., it must be non-uniformly stable) and that the model is also non-differentiable. We then apply this criterion to a probabilistic model (e.g., a Gaussian kernel), in the model of the Kullback-Leibler equation, and show that the probabilistic inference from this model is equivalent to a probabilistic inference from two discrete samples. Our results are particularly strong in situations where the input data is correlated to the underlying distribution, while in other cases the data are not. Our framework is applicable to non-Gaussian distribution and it has strong generalization ability to handle data that is covariially random.

We show that the proposed method achieves state of the art performance on many image classification benchmarks. The accuracy of this algorithm is comparable to previous state of the art methods, e.g., SVMs or Convolutional Neural Networks. The method is a variant of the well-known Kernel SVM, which has been used to model large-scale image classification tasks. We use this method with a new algorithm as a special case, namely in which the learned features are fused to form a single, global, feature-wise binary matrix. To alleviate the computational overhead, our proposed algorithm was trained with a novel deep CNN architecture, which has been trained using only the learned feature maps for segmentation and sparse classification. This allows our algorithm to achieve state-of-the-art performance on the MNIST and CIFAR-10 datasets. To reduce the computational expense, we propose a new approach, i.e., multiple neural network training variants of the same model with different performance. Extensive numerical experiments show that our method outperforms state of the art classifiers on MNIST, CIFAR-10 and FADER datasets.

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Dynamic Programming for Latent Variable Models in Heterogeneous Datasets

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  • Learning Discriminative Kernels by Compressing Them with Random Projections

    Convex Penalized Kernel SVMWe show that the proposed method achieves state of the art performance on many image classification benchmarks. The accuracy of this algorithm is comparable to previous state of the art methods, e.g., SVMs or Convolutional Neural Networks. The method is a variant of the well-known Kernel SVM, which has been used to model large-scale image classification tasks. We use this method with a new algorithm as a special case, namely in which the learned features are fused to form a single, global, feature-wise binary matrix. To alleviate the computational overhead, our proposed algorithm was trained with a novel deep CNN architecture, which has been trained using only the learned feature maps for segmentation and sparse classification. This allows our algorithm to achieve state-of-the-art performance on the MNIST and CIFAR-10 datasets. To reduce the computational expense, we propose a new approach, i.e., multiple neural network training variants of the same model with different performance. Extensive numerical experiments show that our method outperforms state of the art classifiers on MNIST, CIFAR-10 and FADER datasets.


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