4 citations · 7 across the 6 of their papers we have counts for
6 papers
Local Intrinsic Dimensionality of Ground Motion Data for Early Detection of Catastrophic Slope Failure
Yuansan Liu, James Bailey, Antoinette Tordesillas
Local Intrinsic Dimensionality (LID) has shown strong potential for anomaly detection in high-dimensional data, including landslide failure detection in granular media, where early…
Stochastic Diffusion: A Diffusion Probabilistic Model for Stochastic Time Series Forecasting
Yuansan Liu, Sudanthi Wijewickrema, Dongting Hu +3
Recent innovations in diffusion probabilistic models have paved the way for significant progress in image, text and audio generation, leading to their applications in generative ti…
Bidirectional Adversarial Autoencoders for the design of Plasmonic Metasurfaces
Yuansan Liu, Jeygopi Panisilvam, Peter Dower +2
Deep Learning has been a critical part of designing inverse design methods that are computationally efficient and accurate. An example of this is the design of photonic metasurface…
Time Series Representation Learning with Supervised Contrastive Temporal Transformer
Yuansan Liu, Sudanthi Wijewickrema, Christofer Bester +2
Finding effective representations for time series data is a useful but challenging task. Several works utilize self-supervised or unsupervised learning methods to address this. How…
Time-Transformer: Integrating Local and Global Features for Better Time Series Generation (Extended Version)
Yuansan Liu, Sudanthi Wijewickrema, Ang Li +3
Generating time series data is a promising approach to address data deficiency problems. However, it is also challenging due to the complex temporal properties of time series data,…
Pandemic model with data-driven phase detection, a study using COVID-19 data
Yuansan Liu, Saransh Srivastava, Zuo Huang +1
The recent COVID-19 pandemic has promoted vigorous scientific activity in an effort to understand, advice and control the pandemic. Data is now freely available at a staggering rat…