485 citations · 736 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
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,…
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
Xingjun Ma, Bo Li, Yisen Wang +6
Deep Neural Networks (DNNs) have recently been shown to be vulnerable against adversarial examples, which are carefully crafted instances that can mislead DNNs to make errors durin…
Adversarial Generation of Real-time Feedback with Neural Networks for Simulation-based Training
Xingjun Ma, Sudanthi Wijewickrema, Shuo Zhou +4
Simulation-based training (SBT) is gaining popularity as a low-cost and convenient training technique in a vast range of applications. However, for a SBT platform to be fully utili…