4 code implementations • 1 Jun 2015 • Zhiguang Wang, Tim Oates
We used Tiled Convolutional Neural Networks (tiled CNNs) on 20 standard datasets to learn high-level features from the individual and compound GASF-GADF-MTF images.
no code implementations • 8 Jun 2015 • Wei Song, Zhiguang Wang, Yangdong Ye, Ming Fan
Our work provides an analytical framework with several statistical tools to analyze, evaluate and further improve the symbolic dynamics for knowledge discovery in time series.
no code implementations • 8 Jun 2015 • 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 training deep neural networks (DNNs).
no code implementations • 24 Sep 2015 • 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.
no code implementations • 13 Oct 2015 • Zhiguang Wang, Tim Oates, James Lo
We generalized a modified exponentialized estimator by pushing the robust-optimal (RO) index $\lambda$ to $-\infty$ for achieving robustness to outliers by optimizing a quasi-Minimin function.
1 code implementation • 24 Oct 2016 • Lu Liu, Zhiguang Wang
Time series and signals are attracting more attention across statistics, machine learning and pattern recognition as it appears widely in the industry especially in sensor and IoT related research and applications, but few advances has been achieved in effective time series visual analytics and interaction due to its temporal dimensionality and complex dynamics.
1 code implementation • 24 Oct 2016 • Zhiguang Wang, Wei Song, Lu Liu, Fan Zhang, Junxiao Xue, Yangdong Ye, Ming Fan, Mingliang Xu
We propose a new model based on the deconvolutional networks and SAX discretization to learn the representation for multivariate time series.
11 code implementations • 20 Nov 2016 • Zhiguang Wang, Weizhong Yan, Tim Oates
We propose a simple but strong baseline for time series classification from scratch with deep neural networks.
2 code implementations • 31 Mar 2017 • Zhiguang Wang, Jianbo Yang
We proposed a deep learning method for interpretable diabetic retinopathy (DR) detection.
1 code implementation • 13 Sep 2017 • Zhiguang Wang, Chul Gwon, Tim Oates, Adam Iezzi
As the use of cloud computing continues to rise, controlling cost becomes increasingly important.
no code implementations • 24 Apr 2018 • Mehul Parsana, Krishna Poola, Yajun Wang, Zhiguang Wang
The CTR prediction problem is modeled as a supervised recurrent neural network, which naturally model the user history as a sequence of events.
1 code implementation • EMNLP 2020 • Pedro Rodriguez, Paul Crook, Seungwhan Moon, Zhiguang Wang
Assuming a correlation between engagement and user responses such as "liking" messages or asking followup questions, we design a Wizard-of-Oz dialog task that tests the hypothesis that engagement increases when users are presented with facts related to what they know.
1 code implementation • NAACL 2021 • Kai Sun, Seungwhan Moon, Paul Crook, Stephen Roller, Becka Silvert, Bing Liu, Zhiguang Wang, Honglei Liu, Eunjoon Cho, Claire Cardie
Existing dialogue corpora and models are typically designed under two disjoint motives: while task-oriented systems focus on achieving functional goals (e. g., booking hotels), open-domain chatbots aim at making socially engaging conversations.
1 code implementation • EMNLP 2021 • Andrea Madotto, Zhaojiang Lin, Zhenpeng Zhou, Seungwhan Moon, Paul Crook, Bing Liu, Zhou Yu, Eunjoon Cho, Zhiguang Wang
Continual learning in task-oriented dialogue systems can allow us to add new domains and functionalities through time without incurring the high cost of a whole system retraining.
2 code implementations • 26 Mar 2021 • Minghao Liu, Shengqi Ren, Siyuan Ma, Jiahui Jiao, Yizhou Chen, Zhiguang Wang, Wei Song
In this work, we explored a simple extension of the current Transformer Networks with gating, named Gated Transformer Networks (GTN) for the multivariate time series classification problem.
2 code implementations • 10 May 2021 • Zhaojiang Lin, Bing Liu, Seungwhan Moon, Paul Crook, Zhenpeng Zhou, Zhiguang Wang, Zhou Yu, Andrea Madotto, Eunjoon Cho, Rajen Subba
Zero-shot cross-domain dialogue state tracking (DST) enables us to handle task-oriented dialogue in unseen domains without the expense of collecting in-domain data.
1 code implementation • NAACL 2021 • Zhaojiang Lin, Bing Liu, Seungwhan Moon, Paul Crook, Zhenpeng Zhou, Zhiguang Wang, Zhou Yu, Andrea Madotto, Eunjoon Cho, Rajen Subba
Zero-shot cross-domain dialogue state tracking (DST) enables us to handle unseen domains without the expense of collecting in-domain data.
1 code implementation • EMNLP 2021 • Zhaojiang Lin, Bing Liu, Andrea Madotto, Seungwhan Moon, Paul Crook, Zhenpeng Zhou, Zhiguang Wang, Zhou Yu, Eunjoon Cho, Rajen Subba, Pascale Fung
Zero-shot transfer learning for dialogue state tracking (DST) enables us to handle a variety of task-oriented dialogue domains without the expense of collecting in-domain data.
no code implementations • 22 Apr 2022 • Yuanzhen Luo, Qiang Lu, Xilei Hu, Jake Luo, Zhiguang Wang
It then leverages a multi-chromosome NNCGP to represent hidden semantics of all layers of the NN.
no code implementations • 28 Apr 2022 • Baihe He, Qiang Lu, Qingyun Yang, Jake Luo, Zhiguang Wang
So, the search process of GP is usually slow, and the final results could be unstable. To guide GP by these characteristics, we propose a new method for SR, called Taylor genetic programming (TaylorGP) (Code and appendix at https://kgae-cup. github. io/TaylorGP/).
no code implementations • 22 Apr 2024 • Jinglu Song, Qiang Lu, Bozhou Tian, Jingwen Zhang, Jake Luo, Zhiguang Wang
Symbolic regression (SR) is the task of discovering a symbolic expression that fits a given data set from the space of mathematical expressions.