Learning to Compose Uncertain Event-based Features from Data

Learning to Compose Uncertain Event-based Features from Data – We present a novel dataset of events observed during multiple hours in an observable time frame. The dataset consists of events recorded over a three-night period. Each time frame is a different event, and we propose a new feature selection strategy to automatically select relevant events. The resulting dataset will be used to train a convolutional network to predict and predict the most probable future event. We demonstrate that prediction can be done at scale using the full observable time frame using the S4-Net system, and train this model as well using a large-scale dataset. Results show that the proposed approach outperforms the state-of-the-art prediction accuracies.

Generative Adversarial Networks (GANs) have been widely used to generate image based models to learn images, which have been a focus of many researchers in recent years. However, many applications of GANs have not been examined and the literature onGANs in this area is still sparse and very limited. We give an overview of a recently developed deep neural network based onGAN and compare it against other proposed deep learning based onGAN models. We show that deep learning based onGAN models are more robust to variations in the input, as compared to other deep learning based onGAN models trained on the same input. Moreover, we compare various other deep learning based onGAN models which are not used in the literature. Finally, we examine how such a feature rich input representation plays a role in learning deep generative models for classification tasks.

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Learning to Compose Uncertain Event-based Features from Data

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    A Fast Approach to Classification Using Linear and Nonlinear Random FieldsGenerative Adversarial Networks (GANs) have been widely used to generate image based models to learn images, which have been a focus of many researchers in recent years. However, many applications of GANs have not been examined and the literature onGANs in this area is still sparse and very limited. We give an overview of a recently developed deep neural network based onGAN and compare it against other proposed deep learning based onGAN models. We show that deep learning based onGAN models are more robust to variations in the input, as compared to other deep learning based onGAN models trained on the same input. Moreover, we compare various other deep learning based onGAN models which are not used in the literature. Finally, we examine how such a feature rich input representation plays a role in learning deep generative models for classification tasks.


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