PanoqueCa: Popper and Context for Semantic Parsing of Large Categorical Datasets


PanoqueCa: Popper and Context for Semantic Parsing of Large Categorical Datasets – We propose an adaptive method for learning the state-space of a dataset in a semi-supervised manner. The goal is to find the best subset of the input data, which can be used to learn a state-space for a given dataset. We present a neural network model that jointly learns the local and global features of the input data, and is trained in several variants for a given datasets, and then uses a semi-supervised learning approach to learn a sparse representation of the inputs. We show that training the neural network model is in order to maximize the performance of its learned feature representations. We also use this model to learn a structured description of the data, to support the learning process for supervised object classification from the dataset and to support the retrieval of a given dataset from the database. Experiments on a dataset of 12 classes showed that our model has a significant improvement in the classification error rate compared to baselines, and outperforms state of the art methods on MNIST and CIFAR-10.

In most real-world traffic data, the data is typically collected during the day. The road is usually a grid of roads. In most cases, a small number of vehicles are involved in traffic. However, there is a large amount of human-provided information regarding the actual road traffic. In this paper, we are analyzing road traffic data collected during the day in a traffic prediction setting using synthetic and real data. This dataset consists of real traffic data collected during the day. Our goal is to learn the road traffic model in order to predict road traffic traffic in the real world. We design the network for real traffic prediction and model the road traffic model using synthetic data on the road. Our network is trained using state-of-the-art Deep Reinforcement Learning techniques. Experimental results show that our network achieves very good performance on synthetic traffic prediction task.

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PanoqueCa: Popper and Context for Semantic Parsing of Large Categorical Datasets

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    A Comparative Study of Machine Learning Techniques for Road Traffic Speed Prediction from Real Traffic DataIn most real-world traffic data, the data is typically collected during the day. The road is usually a grid of roads. In most cases, a small number of vehicles are involved in traffic. However, there is a large amount of human-provided information regarding the actual road traffic. In this paper, we are analyzing road traffic data collected during the day in a traffic prediction setting using synthetic and real data. This dataset consists of real traffic data collected during the day. Our goal is to learn the road traffic model in order to predict road traffic traffic in the real world. We design the network for real traffic prediction and model the road traffic model using synthetic data on the road. Our network is trained using state-of-the-art Deep Reinforcement Learning techniques. Experimental results show that our network achieves very good performance on synthetic traffic prediction task.


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