Multiset Regression Neural Networks with Input Signals


Multiset Regression Neural Networks with Input Signals – We present an efficient approach for learning sparse vector representations from input signals. Unlike traditional sparse vector representations which typically use a fixed set of labels, our approach does not require labels at all. We show that sparse vectors are flexible representations, allowing the training of networks of arbitrary sizes, with strong bounds on the true number of labels. We then illustrate that a neural network can accurately predict the label accuracy by sampling a sparse vector from a large set of input signals. This study shows a promising strategy for a supervised learning architecture: using such a model for predicting labels, it can be used to predict the true labels with minimal hand-crafted labeling.

The main objective of this paper is to build a new framework for efficient and scalable prediction. First, a set of algorithms is trained jointly with the stochastic gradient method. Then, a stochastic gradient algorithm is proposed based on a deterministic variational model with a Bayes family of random variables. The posterior distribution of the stochastic gradient is used for inference and the random variable is estimated using a polynomial-time Monte Carlo approach. The proposed method is demonstrated with the MNIST, MNIST-2K and CIFAR-10 data sets.

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Multiset Regression Neural Networks with Input Signals

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  • Semantic Font Attribution Using Deep Learning

    Robust Decomposition Based on Robust Compressive BoundsThe main objective of this paper is to build a new framework for efficient and scalable prediction. First, a set of algorithms is trained jointly with the stochastic gradient method. Then, a stochastic gradient algorithm is proposed based on a deterministic variational model with a Bayes family of random variables. The posterior distribution of the stochastic gradient is used for inference and the random variable is estimated using a polynomial-time Monte Carlo approach. The proposed method is demonstrated with the MNIST, MNIST-2K and CIFAR-10 data sets.


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