no code implementations • ICLR 2019 • Xiang Jiang, Mohammad Havaei, Gabriel Chartrand, Hassan Chouaib, Thomas Vincent, Andrew Jesson, Nicolas Chapados, Stan Matwin
Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce.
no code implementations • 15 Dec 2020 • Qicheng Lao, Xiang Jiang, Mohammad Havaei
We propose a hypothesis disparity regularized mutual information maximization~(HDMI) approach to tackle unsupervised hypothesis transfer -- as an effort towards unifying hypothesis transfer learning (HTL) and unsupervised domain adaptation (UDA) -- where the knowledge from a source domain is transferred solely through hypotheses and adapted to the target domain in an unsupervised manner.
1 code implementation • ICML 2020 • Xiang Jiang, Qicheng Lao, Stan Matwin, Mohammad Havaei
We present an approach for unsupervised domain adaptation---with a strong focus on practical considerations of within-domain class imbalance and between-domain class distribution shift---from a class-conditioned domain alignment perspective.
Ranked #1 on Unsupervised Domain Adaptation on Office-Home (Avg accuracy metric)
no code implementations • 9 Mar 2020 • Qicheng Lao, Xiang Jiang, Mohammad Havaei, Yoshua Bengio
Learning in non-stationary environments is one of the biggest challenges in machine learning.
no code implementations • ICLR 2019 • Xiang Jiang, Mohammad Havaei, Farshid Varno, Gabriel Chartrand, Nicolas Chapados, Stan Matwin
Neural networks can learn to extract statistical properties from data, but they seldom make use of structured information from the label space to help representation learning.
no code implementations • 3 Jun 2018 • Xiang Jiang, Mohammad Havaei, Gabriel Chartrand, Hassan Chouaib, Thomas Vincent, Andrew Jesson, Nicolas Chapados, Stan Matwin
Based on the Model-Agnostic Meta-Learning framework (MAML), we introduce the Attentive Task-Agnostic Meta-Learning (ATAML) algorithm for text classification.
no code implementations • 7 May 2017 • Xiang Jiang, Erico N de Souza, Ahmad Pesaranghader, Baifan Hu, Daniel L. Silver, Stan Matwin
Understanding and discovering knowledge from GPS (Global Positioning System) traces of human activities is an essential topic in mobility-based urban computing.
no code implementations • 25 Oct 2016 • Xiang Jiang, Shikui Wei, Ruizhen Zhao, Yao Zhao, Xindong Wu
The underlying assumption is that multiple accounts belonging to the same person contain the same or similar camera fingerprint information.