activity
20182024
most citedDeep Nearest Class Mean Model for Incremental Odor Classification

14 citations · 16 across the 5 of their papers we have counts for

collaborators

5 papers

cs.NI2024

Sustainable Placement with Cost Minimization in Wireless Digital Twin Networks

Yuzhi Zhou, Yaru Fu, Zheng Shi +3

Digital twin (DT) technology has a high potential to satisfy different requirements of the ever-expanding new applications. Nonetheless, the DT placement in wireless digital twin n…

cs.CV2024

WTTFNet: A Weather-Time-Trajectory Fusion Network for Pedestrian Trajectory Prediction in Urban Complex

Ho Chun Wu, Esther Hoi Shan Lau, Paul Yuen +3

Pedestrian trajectory modelling in an urban complex is challenging because pedestrians can have many possible destinations, such as shops, escalators, and attractions. Moreover, we…

cs.AI2023

BG-GAN: Generative AI Enable Representing Brain Structure-Function Connections for Alzheimer's Disease

Tong Zhou, Chen Ding, Changhong Jing +8

The relationship between brain structure and function is critical for revealing the pathogenesis of brain disorders, including Alzheimer's disease (AD). However, mapping brain stru…

cs.LG2019★ 2 cited

POP-CNN: Predicting Odor's Pleasantness with Convolutional Neural Network

Danli Wu, Yu Cheng, Dehan Luo +3

Predicting odor's pleasantness simplifies the evaluation of odors and has the potential to be applied in perfumes and environmental monitoring industry. Classical algorithms for pr…

cs.LG2018★ 14 cited

Deep Nearest Class Mean Model for Incremental Odor Classification

Yu Cheng, Angus Wong, Kevin Hung +3

In recent years, more machine learning algorithms have been applied to odor classification. These odor classification algorithms usually assume that the training datasets are stati…