48 citations · 56 across the 6 of their papers we have counts for
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cs.LG2018★ 1 cited
Energy-Efficient Inference Accelerator for Memory-Augmented Neural Networks on an FPGA
Seongsik Park, Jaehee Jang, Seijoon Kim +1
Memory-augmented neural networks (MANNs) are designed for question-answering tasks. It is difficult to run a MANN effectively on accelerators designed for other neural networks (NN…
cs.LG2017★ 3 cited
Quantized Memory-Augmented Neural Networks
Seongsik Park, Seijoon Kim, Seil Lee +2
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…
cs.LG2016★ 1 cited
An Efficient Approach to Boosting Performance of Deep Spiking Network Training
Seongsik Park, Sang-gil Lee, Hyunha Nam +1
Nowadays deep learning is dominating the field of machine learning with state-of-the-art performance in various application areas. Recently, spiking neural networks (SNNs) have bee…