Convolutional neural network – We present a new technique called recurrent neural network (RNN), which is a neural network based neural model for deep architectures. Despite being trained on a relatively small number of examples in a given dataset, RNN has a significant impact on deep learning tasks like image classification. The problem of robust classification in RNN is a significant limitation in RNN due to its high cost, non-linear operation, and low model size. Here we present a framework for robust classification by learning from small, well supervised examples. We present a novel architecture, called MultiSVM-Net, which consists of two distinct modules: a large-scale RNN based recurrent neural network and a new, multi-task-adaptive recurrent network to learn a small, well-labeled target, while keeping track of target’s behavior and memory usage over a large set of examples. We demonstrate the effectiveness of the proposed approach over a large set of large datasets.

In this paper we address the problem of learning a monotonic programming model when the objective is a continuous non-monotonic function whose function can be represented as a matrix. We demonstrate our approach using a large database which consists of both binary and non-binary data. It is our goal to find a model where the objective function is an efficient learning algorithm that is efficient for training a monotonic program.

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Convolutional neural network

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A Probabilistic Latent Non-Monotonic Programming Model for Data Representation: A Latent Variable ApproachIn this paper we address the problem of learning a monotonic programming model when the objective is a continuous non-monotonic function whose function can be represented as a matrix. We demonstrate our approach using a large database which consists of both binary and non-binary data. It is our goal to find a model where the objective function is an efficient learning algorithm that is efficient for training a monotonic program.