Quantized Memory-Augmented Neural Networks

Understand

Memory-augmented neural networks (MANNs) refer to a class of neural network models equipped with external memory (such as neural Turing machines and memory networks).

• These neural networks outperform conventional recurrent neural networks (RNNs) in terms of learning long-term dependency, allowing them to solve intriguing AI tasks that would otherwise be hard to address.
• This paper concerns the problem of quantizing MANNs.
• Quantization is known to be effective when we deploy deep models on embedded systems with limited resources.