30 citations · 78 across the 9 of their papers we have counts for
9 papers
Distributionally Robust Cross Subject EEG Decoding
Tiehang Duan, Zhenyi Wang, Gianfranco Doretto +3
Recently, deep learning has shown to be effective for Electroencephalography (EEG) decoding tasks. Yet, its performance can be negatively influenced by two key factors: 1) the high…
Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions
Zhenyi Wang, Li Shen, Le Fang +4
The paradigm of machine intelligence moves from purely supervised learning to a more practical scenario when many loosely related unlabeled data are available and labeled data is s…
Improving Task-free Continual Learning by Distributionally Robust Memory Evolution
Zhenyi Wang, Li Shen, Le Fang +3
Task-free continual learning (CL) aims to learn a non-stationary data stream without explicit task definitions and not forget previous knowledge. The widely adopted memory replay a…
Uncertainty Detection and Reduction in Neural Decoding of EEG Signals
Tiehang Duan, Zhenyi Wang, Sheng Liu +2
EEG decoding systems based on deep neural networks have been widely used in decision making of brain computer interfaces (BCI). Their predictions, however, can be unreliable given…
Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awareness
Zhenyi Wang, Tiehang Duan, Le Fang +2
Recognizing new objects by learning from a few labeled examples in an evolving environment is crucial to obtain excellent generalization ability for real-world machine learning sys…
Attention based Writer Independent Handwriting Verification
Mohammad Abuzar Shaikh, Tiehang Duan, Mihir Chauhan +1
The task of writer verification is to provide a likelihood score for whether the queried and known handwritten image samples belong to the same writer or not. Such a task calls for…
Ultra Efficient Transfer Learning with Meta Update for Cross Subject EEG Classification
Tiehang Duan, Mihir Chauhan, Mohammad Abuzar Shaikh +2
The pattern of Electroencephalogram (EEG) signal differs significantly across different subjects, and poses challenge for EEG classifiers in terms of 1) effectively adapting a lear…
Parallel Clustering of Single Cell Transcriptomic Data with Split-Merge Sampling on Dirichlet Process Mixtures
Tiehang Duan, José P. Pinto, Xiaohui Xie
Motivation: With the development of droplet based systems, massive single cell transcriptome data has become available, which enables analysis of cellular and molecular processes a…
Sequential Embedding Induced Text Clustering, a Non-parametric Bayesian Approach
Tiehang Duan, Qi Lou, Sargur N. Srihari +1
Current state-of-the-art nonparametric Bayesian text clustering methods model documents through multinomial distribution on bags of words. Although these methods can effectively ut…