6 citations · 13 across the 10 of their papers we have counts for
8 papers · 1 filter
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…
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…