37 citations · 61 across the 9 of their papers we have counts for
9 papers
-Equivariant Vision Transformer
Renjun Xu, Kaifan Yang, Ke Liu +1
Vision Transformer (ViT) has achieved remarkable performance in computer vision. However, positional encoding in ViT makes it substantially difficult to learn the intrinsic equivar…
S2SNet: A Pretrained Neural Network for Superconductivity Discovery
Ke Liu, Kaifan Yang, Jiahong Zhang +1
Superconductivity allows electrical current to flow without any energy loss, and thus making solids superconducting is a grand goal of physics, material science, and electrical eng…
Personalized Federated Learning with Adaptive Batchnorm for Healthcare
Wang Lu, Jindong Wang, Yiqiang Chen +4
There is a growing interest in applying machine learning techniques to healthcare. Recently, federated learning (FL) is gaining popularity since it allows researchers to train powe…
Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling
Bei Yang, Jie Gu, Ke Liu +4
User Modeling plays an essential role in industry. In this field, task-agnostic approaches, which generate general-purpose representation applicable to diverse downstream user cogn…
Interest-oriented Universal User Representation via Contrastive Learning
Qinghui Sun, Jie Gu, Bei Yang +5
User representation is essential for providing high-quality commercial services in industry. Universal user representation has received many interests recently, with which we can b…
AdaRNN: Adaptive Learning and Forecasting of Time Series
Yuntao Du, Jindong Wang, Wenjie Feng +4
Time series has wide applications in the real world and is known to be difficult to forecast. Since its statistical properties change over time, its distribution also changes tempo…
Exploiting Adapters for Cross-lingual Low-resource Speech Recognition
Wenxin Hou, Han Zhu, Yidong Wang +4
Cross-lingual speech adaptation aims to solve the problem of leveraging multiple rich-resource languages to build models for a low-resource target language. Since the low-resource…
Learning Invariant Representations across Domains and Tasks
Jindong Wang, Wenjie Feng, Chang Liu +5
Being expensive and time-consuming to collect massive COVID-19 image samples to train deep classification models, transfer learning is a promising approach by transferring knowledg…
Learning to Match Distributions for Domain Adaptation
Chaohui Yu, Jindong Wang, Chang Liu +5
When the training and test data are from different distributions, domain adaptation is needed to reduce dataset bias to improve the model's generalization ability. Since it is diff…