375 citations · 2.2k across the 69 of their papers we have counts for
13 papers · 1 filter
SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning
Zhuowei Wang, Jing Jiang, Bo Han +4
Deep learning with noisy labels is a challenging task. Recent prominent methods that build on a specific sample selection (SS) strategy and a specific semi-supervised learning (SSL…
Confusable Learning for Large-class Few-Shot Classification
Bingcong Li, Bo Han, Zhuowei Wang +2
Few-shot image classification is challenging due to the lack of ample samples in each class. Such a challenge becomes even tougher when the number of classes is very large, i.e., t…
Cooperative Heterogeneous Deep Reinforcement Learning
Han Zheng, Pengfei Wei, Jing Jiang +3
Numerous deep reinforcement learning agents have been proposed, and each of them has its strengths and flaws. In this work, we present a Cooperative Heterogeneous Deep Reinforcemen…
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler
Zhining Liu, Pengfei Wei, Jing Jiang +3
Imbalanced learning (IL), i.e., learning unbiased models from class-imbalanced data, is a challenging problem. Typical IL methods including resampling and reweighting were designed…
RatE: Relation-Adaptive Translating Embedding for Knowledge Graph Completion
Hao Huang, Guodong Long, Tao Shen +2
Many graph embedding approaches have been proposed for knowledge graph completion via link prediction. Among those, translating embedding approaches enjoy the advantages of light-w…
BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes
Xueping Peng, Guodong Long, Tao Shen +3
Electronic health records (EHRs) are longitudinal records of a patient's interactions with healthcare systems. A patient's EHR data is organized as a three-level hierarchy from top…
Attribute Propagation Network for Graph Zero-shot Learning
Lu Liu, Tianyi Zhou, Guodong Long +2
The goal of zero-shot learning (ZSL) is to train a model to classify samples of classes that were not seen during training. To address this challenging task, most ZSL methods relat…
Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy
Lu Liu, Tianyi Zhou, Guodong Long +2
We study many-class few-shot (MCFS) problem in both supervised learning and meta-learning settings. Compared to the well-studied many-class many-shot and few-class few-shot problem…
Self-Attention Enhanced Patient Journey Understanding in Healthcare System
Xueping Peng, Guodong Long, Tao Shen +2
Understanding patients' journeys in healthcare system is a fundamental prepositive task for a broad range of AI-based healthcare applications. This task aims to learn an informativ…
A Universal Representation Transformer Layer for Few-Shot Image Classification
Lu Liu, William Hamilton, Guodong Long +2
Few-shot classification aims to recognize unseen classes when presented with only a small number of samples. We consider the problem of multi-domain few-shot image classification,…
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks
Zonghan Wu, Shirui Pan, Guodong Long +3
Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic. A basic assumptio…
Multi-Center Federated Learning: Clients Clustering for Better Personalization
Guodong Long, Ming Xie, Tao Shen +4
Federated learning has received great attention for its capability to train a large-scale model in a decentralized manner without needing to access user data directly. It helps pro…
Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification
Wensi Tang, Guodong Long, Lu Liu +3
The Receptive Field (RF) size has been one of the most important factors for One Dimensional Convolutional Neural Networks (1D-CNNs) on time series classification tasks. Large effo…