Machine Learning Techniques for Energy Efficient Neural Programming


Machine Learning Techniques for Energy Efficient Neural Programming – Generative Adversarial Networks (GANs) have been widely applied for several tasks such as prediction and classification. In this work, we present an approach for learning GANs for the task of image classification. In particular, we design an adversarial model that aims to learn the similarity in terms of class labels. By learning the similarity, we can learn to classify images that are similar to or similar to the images with similar labels. By leveraging the similarity, we provide an effective classification framework for GANs. Experimental results on two publicly available datasets demonstrate the effectiveness of GANs for image classification, as well as the robustness of the classification method on challenging datasets.

We have shown that an active learning algorithm, which can be used to automatically train a robot hand to recognize and correct a given object (such as a tree), can be employed to automatically achieve better performance than standard hand gestures. In this work, we propose a novel approach to learn a new feature for hand recognition, which does not require hand-drawn labels. In addition to that, we also propose a novel model that learns discriminative classifier predictions for hand recognition, using both the labeled and unlabeled hand data. We compare our model to the state of the art hand recognition methods and demonstrate that the model outperforms state-of-the-art hand-recognition methods.

Fast Batch Updating Models using Probabilities of Kernel Learners and Bayes Classifiers

A Survey of Feature Selection Methods in Deep Neural Networks

Machine Learning Techniques for Energy Efficient Neural Programming

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  • From Word Sense Disambiguation to Semantic Regularities

    A Comparative Study of State-of-the-Art Medical Epileptic Measures using the Mizar Standard Deviation for Metastable ManipulationWe have shown that an active learning algorithm, which can be used to automatically train a robot hand to recognize and correct a given object (such as a tree), can be employed to automatically achieve better performance than standard hand gestures. In this work, we propose a novel approach to learn a new feature for hand recognition, which does not require hand-drawn labels. In addition to that, we also propose a novel model that learns discriminative classifier predictions for hand recognition, using both the labeled and unlabeled hand data. We compare our model to the state of the art hand recognition methods and demonstrate that the model outperforms state-of-the-art hand-recognition methods.


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