Degenerating the Gradients


Degenerating the Gradients – We present a novel learning framework for learning discriminant feature vectors that is based on local optimization over a set of hidden vectors. We leverage various statistical techniques to learn local optimization functions such as local search, distance based gradient descent, and the Bayesian gradient descent technique. Our framework significantly outperforms state-of-the-art local optimization methods on an extensive set of datasets. We demonstrate how to use local optimization as a nonlinear learning algorithm to learn discriminant feature vectors that can be learned anytime. Our approach reduces the training and predicting time of state-of-the-art gradient descent based neural networks to a single learning problem, leading to superior performance compared to the state-of-the-art approaches.

We propose a method that is more robust than other methods by learning sparse representations of a domain. This is the first time that the data is sparsely sparse. The key finding of this paper comes from the fact that the sparse representation of the domain is invariant to outliers in the dataset, and thus, this can be used to improve the estimation of the model’s posterior model in the supervised domain. The proposed model learns the sparse representations using linear programming (LP), and the corresponding inference algorithm is implemented using a deep neural network. Experiments conducted on ImageNet with over 1000 labeled images and more than 1000 unlabeled images demonstrate that the proposed model performs well in terms of accuracy, speed, and scalability.

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Degenerating the Gradients

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  • The Sample-Efficient Analysis of Convexity of Bayes-Optimal Covariate Shift

    Unsupervised Unsupervised Domain Adaptation for Robust Low-Rank Metric LearningWe propose a method that is more robust than other methods by learning sparse representations of a domain. This is the first time that the data is sparsely sparse. The key finding of this paper comes from the fact that the sparse representation of the domain is invariant to outliers in the dataset, and thus, this can be used to improve the estimation of the model’s posterior model in the supervised domain. The proposed model learns the sparse representations using linear programming (LP), and the corresponding inference algorithm is implemented using a deep neural network. Experiments conducted on ImageNet with over 1000 labeled images and more than 1000 unlabeled images demonstrate that the proposed model performs well in terms of accuracy, speed, and scalability.


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