Search Results for author:
Found 8 papers, 1 papers with code
Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification
Current deep learning based text classification methods are limited by their ability to achieve fast learning and generalization when the data is scarce.
Hypothesis Disparity Regularized Mutual Information Maximization
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.
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
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)
Continuous Domain Adaptation with Variational Domain-Agnostic Feature Replay
Learning in non-stationary environments is one of the biggest challenges in machine learning.
Learning to Learn with Conditional Class Dependencies
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.
On the Importance of Attention in Meta-Learning for Few-Shot Text Classification
Based on the Model-Agnostic Meta-Learning framework (MAML), we introduce the Attentive Task-Agnostic Meta-Learning (ATAML) algorithm for text classification.
TrajectoryNet: An Embedded GPS Trajectory Representation for Point-based Classification Using Recurrent Neural Networks
Understanding and discovering knowledge from GPS (Global Positioning System) traces of human activities is an essential topic in mobility-based urban computing.
Camera Fingerprint: A New Perspective for Identifying User's Identity
The underlying assumption is that multiple accounts belonging to the same person contain the same or similar camera fingerprint information.
