5 citations · 5 across the 2 of their papers we have counts for
6 papers
Accelerated Linearized Laplace Approximation for Bayesian Deep Learning
Zhijie Deng, Feng Zhou, Jun Zhu
Laplace approximation (LA) and its linearized variant (LLA) enable effortless adaptation of pretrained deep neural networks to Bayesian neural networks. The generalized Gauss-Newto…
Nonlinear Hawkes Processes in Time-Varying System
Feng Zhou, Quyu Kong, Yixuan Zhang +2
Hawkes processes are a class of point processes that have the ability to model the self- and mutual-exciting phenomena. Although the classic Hawkes processes cover a wide range of…
High-fidelity Prediction of Megapixel Longitudinal Phase-space Images of Electron Beams using Encoder-Decoder Neural Networks
Jun Zhu, Ye Chen, Frank Brinker +3
Modeling of large-scale research facilities is extremely challenging due to complex physical processes and engineering problems. Here, we adopt a data-driven approach to model the…
Efficient Inference of Flexible Interaction in Spiking-neuron Networks
Feng Zhou, Yixuan Zhang, Jun Zhu
Hawkes process provides an effective statistical framework for analyzing the time-dependent interaction of neuronal spiking activities. Although utilized in many real applications,…
DashNet: A Hybrid Artificial and Spiking Neural Network for High-speed Object Tracking
Zheyu Yang, Yujie Wu, Guanrui Wang +5
Computer-science-oriented artificial neural networks (ANNs) have achieved tremendous success in a variety of scenarios via powerful feature extraction and high-precision data opera…
Direct Training for Spiking Neural Networks: Faster, Larger, Better
Yujie Wu, Lei Deng, Guoqi Li +2
Spiking neural networks (SNNs) that enables energy efficient implementation on emerging neuromorphic hardware are gaining more attention. Yet now, SNNs have not shown competitive p…