482 citations · 744 across the 13 of their papers we have counts for
5 papers · 1 filter
Adopting Robustness and Optimality in Fitting and Learning
Zhiguang Wang, Tim Oates, James Lo
We generalized a modified exponentialized estimator by pushing the robust-optimal (RO) index to for achieving robustness to outliers by optimizing a quasi-Minimin fun…
Spatially Encoding Temporal Correlations to Classify Temporal Data Using Convolutional Neural Networks
Zhiguang Wang, Tim Oates
We propose an off-line approach to explicitly encode temporal patterns spatially as different types of images, namely, Gramian Angular Fields and Markov Transition Fields. This ena…
Empirical Studies on Symbolic Aggregation Approximation Under Statistical Perspectives for Knowledge Discovery in Time Series
Wei Song, Zhiguang Wang, Yangdong Ye +1
Symbolic Aggregation approXimation (SAX) has been the de facto standard representation methods for knowledge discovery in time series on a number of tasks and applications. So far,…
Adaptive Normalized Risk-Averting Training For Deep Neural Networks
Zhiguang Wang, Tim Oates, James Lo
This paper proposes a set of new error criteria and learning approaches, Adaptive Normalized Risk-Averting Training (ANRAT), to attack the non-convex optimization problem in traini…
Imaging Time-Series to Improve Classification and Imputation
Zhiguang Wang, Tim Oates
Inspired by recent successes of deep learning in computer vision, we propose a novel framework for encoding time series as different types of images, namely, Gramian Angular Summat…