29 citations · 32 across the 5 of their papers we have counts for
5 papers
Learning Point Processes using Recurrent Graph Network
Saurabh Dash, Xueyuan She, Saibal Mukhopadhyay
We present a novel Recurrent Graph Network (RGN) approach for predicting discrete marked event sequences by learning the underlying complex stochastic process. Using the framework…
A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection
Biswadeep Chakraborty, Xueyuan She, Saibal Mukhopadhyay
This paper proposes a Fully Spiking Hybrid Neural Network (FSHNN) for energy-efficient and robust object detection in resource-constrained platforms. The network architecture is ba…
Improving Robustness of ReRAM-based Spiking Neural Network Accelerator with Stochastic Spike-timing-dependent-plasticity
Xueyuan She, Yun Long, Saibal Mukhopadhyay
Spike-timing-dependent-plasticity (STDP) is an unsupervised learning algorithm for spiking neural network (SNN), which promises to achieve deeper understanding of human brain and m…
ScieNet: Deep Learning with Spike-assisted Contextual Information Extraction
Xueyuan She, Yun Long, Daehyun Kim +1
Deep neural networks (DNNs) provide high image classification accuracy, but experience significant performance degradation when perturbation from various sources are present in the…
HybridNet: Integrating Model-based and Data-driven Learning to Predict Evolution of Dynamical Systems
Yun Long, Xueyuan She, Saibal Mukhopadhyay
The robotic systems continuously interact with complex dynamical systems in the physical world. Reliable predictions of spatiotemporal evolution of these dynamical systems, with li…