Search Results for author:
Found 8 papers, 2 papers with code
Predicting Fine-grained Behavioral and Psychological Symptoms of Dementia Based on Machine Learning and Smart Wearable Devices
This study aims to fill this gap by presenting a novel personalized framework for BPSD prediction, utilizing physiological signals from smart wearable devices.
DL-KDD: Dual-Light Knowledge Distillation for Action Recognition in the Dark
The results highlight the advantages of our knowledge distillation framework in dark human action recognition.
CAAP: Class-Dependent Automatic Data Augmentation Based On Adaptive Policies For Time Series
We propose a novel deep learning-based approach called Class-dependent Automatic Adaptive Policies (CAAP) framework to overcome the notable class-dependent bias problem while maintaining the overall improvement in time-series data augmentation.
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
In this survey, we offer a comprehensive and up-to-date review of large models tailored (or adapted) for time series and spatio-temporal data, spanning four key facets: data types, model categories, model scopes, and application areas/tasks.
Multi-view knowledge distillation transformer for human action recognition
Recently, Transformer-based methods have been utilized to improve the performance of human action recognition.
FLAG: Fast Label-Adaptive Aggregation for Multi-label Classification in Federated Learning
Federated learning aims to share private data to maximize the data utility without privacy leakage.
Snippet Policy Network for Multi-class Varied-length ECG Early Classification
To the best of our knowledge, this is the first work focusing on solving the cardiovascular early classification problem based on varied-length ECG data.
AutoAudit: Mining Accounting and Time-Evolving Graphs
How can we spot money laundering in large-scale graph-like accounting datasets?
Social and Information Networks
