Search Results for author:
Found 11 papers, 3 papers with code
Generating Hard-Negative Out-of-Scope Data with ChatGPT for Intent Classification
Intent classifiers must be able to distinguish when a user's utterance does not belong to any supported intent to avoid producing incorrect and unrelated system responses.
Feature Affinity Assisted Knowledge Distillation and Quantization of Deep Neural Networks on Label-Free Data
In this paper, we propose a feature affinity (FA) assisted knowledge distillation (KD) method to improve quantization-aware training of deep neural networks (DNN).
Channel Pruning In Quantization-aware Training: An Adaptive Projection-gradient Descent-shrinkage-splitting Method
We propose an adaptive projection-gradient descent-shrinkage-splitting method (APGDSSM) to integrate penalty based channel pruning into quantization-aware training (QAT).
An integrated recurrent neural network and regression model with spatial and climatic couplings for vector-borne disease dynamics
We developed an integrated recurrent neural network and nonlinear regression spatio-temporal model for vector-borne disease evolution.
DPVI: A Dynamic-Weight Particle-Based Variational Inference Framework
We develop a general Dynamic-weight Particle-based Variational Inference (DPVI) framework according to a novel continuous composite flow, which evolves the positions and weights of particles simultaneously.
A Deep Learning Approach for Macroscopic Energy Consumption Prediction with Microscopic Quality for Electric Vehicles
For this work, we leveraged a high-performance, agent-based transportation tool to model trips that occur in the Greater Chicago region under various scenario changes, along with physics-based modeling and simulation tools to provide high-fidelity energy consumption values.
A Real-Time Energy and Cost Efficient Vehicle Route Assignment Neural Recommender System
This paper presents a neural network recommender system algorithm for assigning vehicles to routes based on energy and cost criteria.
A Spatial-Temporal Graph Based Hybrid Infectious Disease Model with Application to COVID-19
For edge feature, we design an RNN model to capture the neighboring effect and regularize the landscape of loss function so that local minima are effective and robust for prediction.
An Integrated Approach to Produce Robust Models with High Efficiency
To solve the problems that adversarial training jeopardizes DNNs' accuracy on clean images and the struture of sparsity, we design a trade-off loss function that helps DNNs preserve their natural accuracy and improve the channel sparsity.
A Recurrent Neural Network and Differential Equation Based Spatiotemporal Infectious Disease Model with Application to COVID-19
Modeling the trend of infection and real-time forecasting of cases can help decision making and control of the disease spread.
A Study on Graph-Structured Recurrent Neural Networks and Sparsification with Application to Epidemic Forecasting
We study epidemic forecasting on real-world health data by a graph-structured recurrent neural network (GSRNN).
