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End-To-End Memory Networks
We introduce a neural network with a recurrent attention model over a possibly large external memory. The architecture is a form of Memory Network (Weston et al., 2015) but unlike the model in thatExpand
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Spectral Networks and Locally Connected Networks on Graphs
Convolutional Neural Networks are extremely efficient architectures in image and audio recognition tasks, thanks to their ability to exploit the local translational invariance of signal classes overExpand
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Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
In this paper we introduce a generative parametric model capable of producing high quality samples of natural images. Our approach uses a cascade of convolutional networks within a Laplacian pyramidExpand
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Personalizing Dialogue Agents: I have a dog, do you have pets too?
Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating. In this work we present the task of makingExpand
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Incremental gradient on the Grassmannian for online foreground and background separation in subsampled video
It has recently been shown that only a small number of samples from a low-rank matrix are necessary to reconstruct the entire matrix. We bring this to bear on computer vision problems that utilizeExpand
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Geometric Deep Learning: Going beyond Euclidean data
Many scientific fields study data with an underlying structure that is non-Euclidean. Some examples include social networks in computational social sciences, sensor networks in communications,Expand
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Learning Multiagent Communication with Backpropagation
Many tasks in AI require the collaboration of multiple agents. Typically, the communication protocol between agents is manually specified and not altered during training. In this paper we explore aExpand
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A Randomized Algorithm for Principal Component Analysis
Principal component analysis (PCA) requires the computation of a low-rank approximation to a matrix containing the data being analyzed. In many applications of PCA, the best possible accuracy of anyExpand
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Tracking the World State with Recurrent Entity Networks
We introduce a new model, the Recurrent Entity Network (EntNet). It is equipped with a dynamic long-term memory which allows it to maintain and update a rep- resentation of the state of the world asExpand
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Optimizing the Latent Space of Generative Networks
Generative Adversarial Networks (GANs) have been shown to be able to sample impressively realistic images. GAN training consists of a saddle point optimization problem that can be thought of as anExpand
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