Semantic Parsing with Long Short-Term Memory


Semantic Parsing with Long Short-Term Memory – Recent data indicate that neural networks can be trained to learn discriminative representations of natural images. In this paper, we present a deep neural network model trained in visual perception to automatically learn semantic relationships and learn to predict images that are similar to a visual subject. Specifically, we train a network to learn to predict the relationship between images and the object objects it is related to, which can be useful for training a new image category (and therefore for learning relevant features for the subsequent categories). We also show that the learned semantic representations can capture similarities in object categories with respect to other objects. We evaluate our model on two visual tasks and show that the semantic representations captured by our model are comparable, compared to the visual images.

We propose a novel deep learning algorithm, which is capable of learning to generate semantic annotations from hand crafted images. A well-known technique is to pre-trained deep network by using a novel weight loss technique and then performing a set of CNNs for learning an image using this strategy. However, we cannot guarantee that the learned image will produce a semantic annotation, since the weights of the CNNs may grow to negative values. To resolve this issue, we propose a novel deep neural network, which is not trained on handcrafted images. The model can train both independently and jointly, while achieving better performance than standard CNNs. We further give an example of how to utilize this type of input to produce semantic annotations for a dataset of hand-crafted hand-created images. We demonstrate our model on a standard benchmark dataset and demonstrate that it significantly outperforms the state-of-the-art annotation method of the same dataset on both synthetic and real world data sets.

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Semantic Parsing with Long Short-Term Memory

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    Improving Variational Auto-encoder in Reading Comprehension Using Lexical SimilarityWe propose a novel deep learning algorithm, which is capable of learning to generate semantic annotations from hand crafted images. A well-known technique is to pre-trained deep network by using a novel weight loss technique and then performing a set of CNNs for learning an image using this strategy. However, we cannot guarantee that the learned image will produce a semantic annotation, since the weights of the CNNs may grow to negative values. To resolve this issue, we propose a novel deep neural network, which is not trained on handcrafted images. The model can train both independently and jointly, while achieving better performance than standard CNNs. We further give an example of how to utilize this type of input to produce semantic annotations for a dataset of hand-crafted hand-created images. We demonstrate our model on a standard benchmark dataset and demonstrate that it significantly outperforms the state-of-the-art annotation method of the same dataset on both synthetic and real world data sets.


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