Semi-Automatic Construction of Large-Scale Data Sets for Robust Online Pricing


Semi-Automatic Construction of Large-Scale Data Sets for Robust Online Pricing – This paper presents a system-level optimization approach for the first real-time deployment of deep neural network networks, in which agents interact with objects. This approach is based on a combination of state and action prediction. The state prediction refers to predicting the next action and provides a way to make predictions while the agent has to learn the prior. The goal of this paper is to apply this approach to the real-time deployment of deep learning algorithms from a large-scale data repository. To the best of our knowledge, this first deployment of a deep learning system on a publicly available dataset has not seen a single user deploy a single neural network, with as few as 40 agents deployed to the dataset. However, we were able to successfully deploy an agent using only four agents, and the agent is being deployed on an external data set for the purpose of training its object recognition capabilities. Experimental results of experiments show that our approach outperforms state-of-the-art deep learning algorithms for the task of object recognition on both synthetic and real-world data.

We present Deep Attention, a computer vision framework for learning visual attention in deep visual attention systems. Our model learns to focus attention on salient objects and to make predictions to make them more relevant to the user’s attention system. Specifically, we use convolutional neural networks to learn to process two inputs at the same time for a given target object. The outputs learned by these network models are then used to model the object’s location and orientation. Experiments show that our model is capable of learning visual attention models that capture visual attention, and achieves state-of-the-art performance when compared to the state of the art models. It is evaluated on a large-scale benchmark, and compares to several state-of-the-art models. Our network models achieve a large improvement in the recognition accuracy over the state-of-the-art models, and we report an improvement for recognition accuracy on a set of challenging visual object recognition benchmarks.

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Semi-Automatic Construction of Large-Scale Data Sets for Robust Online Pricing

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  • Deep learning and financial data fusion via structural label space mapping

    Neural Architectures of Visual AttentionWe present Deep Attention, a computer vision framework for learning visual attention in deep visual attention systems. Our model learns to focus attention on salient objects and to make predictions to make them more relevant to the user’s attention system. Specifically, we use convolutional neural networks to learn to process two inputs at the same time for a given target object. The outputs learned by these network models are then used to model the object’s location and orientation. Experiments show that our model is capable of learning visual attention models that capture visual attention, and achieves state-of-the-art performance when compared to the state of the art models. It is evaluated on a large-scale benchmark, and compares to several state-of-the-art models. Our network models achieve a large improvement in the recognition accuracy over the state-of-the-art models, and we report an improvement for recognition accuracy on a set of challenging visual object recognition benchmarks.


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