6 citations · 13 across the 10 of their papers we have counts for
10 papers
Feature Affinity Assisted Knowledge Distillation and Quantization of Deep Neural Networks on Label-Free Data
Zhijian Li, Biao Yang, Penghang Yin +2
In this paper, we propose a feature affinity (FA) assisted knowledge distillation (KD) method to improve quantization-aware training of deep neural networks (DNN). The FA loss on i…
Channel Pruning In Quantization-aware Training: An Adaptive Projection-gradient Descent-shrinkage-splitting Method
Zhijian Li, Jack Xin
We propose an adaptive projection-gradient descent-shrinkage-splitting method (APGDSSM) to integrate penalty based channel pruning into quantization-aware training (QAT). APGDSSM c…
An integrated recurrent neural network and regression model with spatial and climatic couplings for vector-borne disease dynamics
Zhijian Li, Jack Xin, Guofa Zhou
We developed an integrated recurrent neural network and nonlinear regression spatio-temporal model for vector-borne disease evolution. We take into account climate data and seasona…
DPVI: A Dynamic-Weight Particle-Based Variational Inference Framework
Chao Zhang, Zhijian Li, Hui Qian +1
The recently developed Particle-based Variational Inference (ParVI) methods drive the empirical distribution of a set of \emph{fixed-weight} particles towards a given target distri…
A Deep Learning Approach for Macroscopic Energy Consumption Prediction with Microscopic Quality for Electric Vehicles
Ayman Moawad, Krishna Murthy Gurumurthy, Omer Verbas +4
This paper presents a machine learning approach to model the electric consumption of electric vehicles at macroscopic level, i.e., in the absence of a speed profile, while preservi…
A Real-Time Energy and Cost Efficient Vehicle Route Assignment Neural Recommender System
Ayman Moawad, Zhijian Li, Ines Pancorbo +6
This paper presents a neural network recommender system algorithm for assigning vehicles to routes based on energy and cost criteria. In this work, we applied this new approach to…
A Spatial-Temporal Graph Based Hybrid Infectious Disease Model with Application to COVID-19
Yunling Zheng, Zhijian Li, Jack Xin +1
As the COVID-19 pandemic evolves, reliable prediction plays an important role for policy making. The classical infectious disease model SEIR (susceptible-exposed-infectious-recover…
An Integrated Approach to Produce Robust Models with High Efficiency
Zhijian Li, Bao Wang, Jack Xin
Deep Neural Networks (DNNs) needs to be both efficient and robust for practical uses. Quantization and structure simplification are promising ways to adapt DNNs to mobile devices,…
A Recurrent Neural Network and Differential Equation Based Spatiotemporal Infectious Disease Model with Application to COVID-19
Zhijian Li, Yunling Zheng, Jack Xin +1
The outbreaks of Coronavirus Disease 2019 (COVID-19) have impacted the world significantly. Modeling the trend of infection and real-time forecasting of cases can help decision mak…
A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting
Zhijian Li, Xiyang Luo, Bao Wang +2
We study epidemic forecasting on real-world health data by a graph-structured recurrent neural network (GSRNN). We achieve state-of-the-art forecasting accuracy on the benchmark CD…