449 citations · 727 across the 31 of their papers we have counts for
5 papers · 1 filter
PTab: Using the Pre-trained Language Model for Modeling Tabular Data
Guang Liu, Jie Yang, Ledell Wu
Tabular data is the foundation of the information age and has been extensively studied. Recent studies show that neural-based models are effective in learning contextual representa…
A Review of Semi Supervised Learning Theories and Recent Advances
Enmei Tu, Jie Yang
Semi-supervised learning, which has emerged from the beginning of this century, is a new type of learning method between traditional supervised learning and unsupervised learning.…
Learning Data-adaptive Nonparametric Kernels
Fanghui Liu, Xiaolin Huang, Chen Gong +2
In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adap…
Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa
Jie Yang, Thomas Drake, Andreas Damianou +1
This paper presents a generic Bayesian framework that enables any deep learning model to actively learn from targeted crowds. Our framework inherits from recent advances in Bayesia…
A Graph-Based Semi-Supervised k Nearest-Neighbor Method for Nonlinear Manifold Distributed Data Classification
Enmei Tu, Yaqian Zhang, Lin Zhu +2
Nearest Neighbors (NN) is one of the most widely used supervised learning algorithms to classify Gaussian distributed data, but it does not achieve good results when it is a…