Convolutional Neural Networks with Binary Synapse Detection

Convolutional Neural Networks with Binary Synapse Detection – In this paper, we propose a novel nonparametric Bayesian method for finding posterior estimates for binary ensemble models. This method utilizes sparse binary-valued likelihoods, which are a type of Bayesian network where the posterior information is derived through the posterior-size estimates extracted from the binary distributions. Experiments on various datasets show the superiority of the proposed method over state-of-the-art Bayesian methods.

We have a paper which proposes an unsupervised CNN-based model for the stochastic and semi-supervised learning of discrete Gaussian graphical models. We use a simple convex optimization method to perform inference of the models and propose a fast and flexible framework based on an ensemble of a small but discrete set of Gaussian graphical models. Our empirical evaluation also shows improvement compared to an iterative model, and our learning method is not based on a discrete model but on a more complex one. The proposed method is tested on a dataset of MNIST, and on a dataset of the MNIST dataset.

In this paper, we tackle the problem of classification of multi-class multi-task learning. We first propose a classification method to perform classification in the presence of unseen classes. Then we extend it to handle cases with nonlinearities in the classification problem. The proposed classification method is based on an optimization of the model parameters and our main goal is to provide efficient learning methods for classification. The main result of our method is to improve the classification performance on the large dataset collected using ImageNet. Furthermore, we use an adaptive hashing algorithm to estimate the hash function parameters with a minimal loss and we use it to reduce the variance. Experimental results demonstrate the effectiveness of our approach.

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Convolutional Neural Networks with Binary Synapse Detection

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  • Identifying and Ranking Images from Streaming Images

    Tensor-based transfer learning for image recognitionIn this paper, we tackle the problem of classification of multi-class multi-task learning. We first propose a classification method to perform classification in the presence of unseen classes. Then we extend it to handle cases with nonlinearities in the classification problem. The proposed classification method is based on an optimization of the model parameters and our main goal is to provide efficient learning methods for classification. The main result of our method is to improve the classification performance on the large dataset collected using ImageNet. Furthermore, we use an adaptive hashing algorithm to estimate the hash function parameters with a minimal loss and we use it to reduce the variance. Experimental results demonstrate the effectiveness of our approach.


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