485 citations · 736 across the 11 of their papers we have counts for
11 papers
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,…
Segmented Pairwise Distance for Time Series with Large Discontinuities
Jiabo He, Sarah Erfani, Sudanthi Wijewickrema +2
Time series with large discontinuities are common in many scenarios. However, existing distance-based algorithms (e.g., DTW and its derivative algorithms) may perform poorly in mea…
Learning Non-Unique Segmentation with Reward-Penalty Dice Loss
Jiabo He, Sarah Erfani, Sudanthi Wijewickrema +2
Semantic segmentation is one of the key problems in the field of computer vision, as it enables computer image understanding. However, most research and applications of semantic se…
Dimensionality-Driven Learning with Noisy Labels
Xingjun Ma, Yisen Wang, Michael E. Houle +5
Datasets with significant proportions of noisy (incorrect) class labels present challenges for training accurate Deep Neural Networks (DNNs). We propose a new perspective for under…
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…
Providing Effective Real-time Feedback in Simulation-based Surgical Training
Xingjun Ma, Sudanthi Wijewickrema, Yun Zhou +3
Virtual reality simulation is becoming popular as a training platform in surgical education. However, one important aspect of simulation-based surgical training that has not receiv…
Feedback Techniques in Computer-Based Simulation Training: A Survey
Sudanthi Wijewickrema, Xingjun Ma, James Bailey +2
Computer-based simulation training (CBST) is gaining popularity in a vast range of applications such as surgery, rehabilitation therapy, military applications, and driver/pilot tra…
Finding Influentials in Twitter: A Temporal Influence Ranking Model
Xingjun Ma, Chunping Li, James Bailey +1
With the growing popularity of online social media, identifying influential users in these social networks has become very popular. Existing works have studied user attributes, net…
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…