192 citations · 944 across the 33 of their papers we have counts for
15 papers · 1 filter
Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting
Mingkui Tan, Guohao Chen, Jiaxiang Wu +4
Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and test data by adapting a given model w.r.t. any test sample. Although recent TTA has sh…
Multi-scale Transformer Pyramid Networks for Multivariate Time Series Forecasting
Yifan Zhang, Rui Wu, Sergiu M. Dascalu +1
Multivariate Time Series (MTS) forecasting involves modeling temporal dependencies within historical records. Transformers have demonstrated remarkable performance in MTS forecasti…
Towards Stable Test-Time Adaptation in Dynamic Wild World
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang +4
Test-time adaptation (TTA) has shown to be effective at tackling distribution shifts between training and testing data by adapting a given model on test samples. However, the onlin…
Free Lunch for Domain Adversarial Training: Environment Label Smoothing
YiFan Zhang, Xue Wang, Jian Liang +4
A fundamental challenge for machine learning models is how to generalize learned models for out-of-distribution (OOD) data. Among various approaches, exploiting invariant features…
Efficient Test-Time Model Adaptation without Forgetting
Shuaicheng Niu, Jiaxiang Wu, Yifan Zhang +4
Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and testing data by adapting a given model w.r.t. any testing sample. This task is particu…
AdaXpert: Adapting Neural Architecture for Growing Data
Shuaicheng Niu, Jiaxiang Wu, Guanghui Xu +5
In real-world applications, data often come in a growing manner, where the data volume and the number of classes may increase dynamically. This will bring a critical challenge for…
No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data
Mi Luo, Fei Chen, Dapeng Hu +3
A central challenge in training classification models in the real-world federated system is learning with non-IID data. To cope with this, most of the existing works involve enforc…
How Well Does Self-Supervised Pre-Training Perform with Streaming Data?
Dapeng Hu, Shipeng Yan, Qizhengqiu Lu +6
Prior works on self-supervised pre-training focus on the joint training scenario, where massive unlabeled data are assumed to be given as input all at once, and only then is a lear…
Learning Defense Transformers for Counterattacking Adversarial Examples
Jincheng Li, Jiezhang Cao, Yifan Zhang +2
Deep neural networks (DNNs) are vulnerable to adversarial examples with small perturbations. Adversarial defense thus has been an important means which improves the robustness of D…
Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning
Yifan Zhang, Peilin Zhao, Qingyao Wu +3
Portfolio Selection is an important real-world financial task and has attracted extensive attention in artificial intelligence communities. This task, however, has two main difficu…
ForecastNet: A Time-Variant Deep Feed-Forward Neural Network Architecture for Multi-Step-Ahead Time-Series Forecasting
Joel Janek Dabrowski, YiFan Zhang, Ashfaqur Rahman
Recurrent and convolutional neural networks are the most common architectures used for time series forecasting in deep learning literature. These networks use parameter sharing by…
Online Adaptive Asymmetric Active Learning with Limited Budgets
Yifan Zhang, Peilin Zhao, Shuaicheng Niu +4
Online Active Learning (OAL) aims to manage unlabeled datastream by selectively querying the label of data. OAL is applicable to many real-world problems, such as anomaly detection…
Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis
Yifan Zhang, Ying Wei, Peilin Zhao +4
Deep learning based medical image diagnosis has shown great potential in clinical medicine. However, it often suffers two major difficulties in practice: 1) only limited labeled sa…
Multi-marginal Wasserstein GAN
Jiezhang Cao, Langyuan Mo, Yifan Zhang +3
Multiple marginal matching problem aims at learning mappings to match a source domain to multiple target domains and it has attracted great attention in many applications, such as…
Adaptive Cost-sensitive Online Classification
Peilin Zhao, Yifan Zhang, Min Wu +3
Cost-Sensitive Online Classification has drawn extensive attention in recent years, where the main approach is to directly online optimize two well-known cost-sensitive metrics: (i…