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• W2949626814 abstract "Despite the recent achievements in machine learning, we are still very far from achieving real artificial intelligence. In this paper, we discuss the limitations of standard deep learning approaches and show that some of these limitations can be overcome by learning how to grow the complexity of a model in a structured way. Specifically, we study the simplest sequence prediction problems that are beyond the scope of what is learnable with standard recurrent networks, algorithmically generated sequences which can only be learned by models which have the capacity to count and to memorize sequences. We show that some basic algorithms can be learned from sequential data using a recurrent network associated with a trainable memory." @default.
• W2949626814 created "2019-06-27" @default.
• W2949626814 creator A5020917394 @default.
• W2949626814 creator A5041693326 @default.
• W2949626814 date "2015-03-03" @default.
• W2949626814 modified "2023-10-01" @default.
• W2949626814 title "Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets" @default.
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