3 citations · 3 across the 1 of their papers we have counts for
3 papers
cs.NE2020
T2FSNN: Deep Spiking Neural Networks with Time-to-first-spike Coding
Seongsik Park, Seijoon Kim, Byunggook Na +1
Spiking neural networks (SNNs) have gained considerable interest due to their energy-efficient characteristics, yet lack of a scalable training algorithm has restricted their appli…
cs.CV2019
Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection
Seijoon Kim, Seongsik Park, Byunggook Na +1
Over the past decade, deep neural networks (DNNs) have demonstrated remarkable performance in a variety of applications. As we try to solve more advanced problems, increasing deman…
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…