479 citations · 1.9k across the 94 of their papers we have counts for
23 papers · 1 filter
On Knowledge Editing in Federated Learning: Perspectives, Challenges, and Future Directions
Leijie Wu, Song Guo, Junxiao Wang +3
As Federated Learning (FL) has gained increasing attention, it has become widely acknowledged that straightforwardly applying stochastic gradient descent (SGD) on the overall frame…
FS-Real: Towards Real-World Cross-Device Federated Learning
Daoyuan Chen, Dawei Gao, Yuexiang Xie +5
Federated Learning (FL) aims to train high-quality models in collaboration with distributed clients while not uploading their local data, which attracts increasing attention in bot…
Dimensionality-Varying Diffusion Process
Han Zhang, Ruili Feng, Zhantao Yang +7
Diffusion models, which learn to reverse a signal destruction process to generate new data, typically require the signal at each step to have the same dimension. We argue that, con…
Neural Dependencies Emerging from Learning Massive Categories
Ruili Feng, Kecheng Zheng, Kai Zhu +7
This work presents two astonishing findings on neural networks learned for large-scale image classification. 1) Given a well-trained model, the logits predicted for some category c…
Efficient End-to-End AutoML via Scalable Search Space Decomposition
Yang Li, Yu Shen, Wentao Zhang +2
End-to-end AutoML has attracted intensive interests from both academia and industry which automatically searches for ML pipelines in a space induced by feature engineering, algorit…
Principled Knowledge Extrapolation with GANs
Ruili Feng, Jie Xiao, Kecheng Zheng +4
Human can extrapolate well, generalize daily knowledge into unseen scenarios, raise and answer counterfactual questions. To imitate this ability via generative models, previous wor…
FederatedScope-GNN: Towards a Unified, Comprehensive and Efficient Package for Federated Graph Learning
Zhen Wang, Weirui Kuang, Yuexiang Xie +4
The incredible development of federated learning (FL) has benefited various tasks in the domains of computer vision and natural language processing, and the existing frameworks suc…
FederatedScope: A Flexible Federated Learning Platform for Heterogeneity
Yuexiang Xie, Zhen Wang, Dawei Gao +6
Although remarkable progress has been made by existing federated learning (FL) platforms to provide infrastructures for development, these platforms may not well tackle the challen…
Learning to be a Statistician: Learned Estimator for Number of Distinct Values
Renzhi Wu, Bolin Ding, Xu Chu +4
Estimating the number of distinct values (NDV) in a column is useful for many tasks in database systems, such as columnstore compression and data profiling. In this work, we focus…
Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI
Jiangchao Yao, Shengyu Zhang, Yang Yao +15
Influenced by the great success of deep learning via cloud computing and the rapid development of edge chips, research in artificial intelligence (AI) has shifted to both of the co…
M6-10T: A Sharing-Delinking Paradigm for Efficient Multi-Trillion Parameter Pretraining
Junyang Lin, An Yang, Jinze Bai +9
Recent expeditious developments in deep learning algorithms, distributed training, and even hardware design for large models have enabled training extreme-scale models, say GPT-3 a…
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition
Yang Li, Yu Shen, Wentao Zhang +8
End-to-end AutoML has attracted intensive interests from both academia and industry, which automatically searches for ML pipelines in a space induced by feature engineering, algori…
Learning to Rehearse in Long Sequence Memorization
Zhu Zhang, Chang Zhou, Jianxin Ma +4
Existing reasoning tasks often have an important assumption that the input contents can be always accessed while reasoning, requiring unlimited storage resources and suffering from…
Reliable Adversarial Distillation with Unreliable Teachers
Jianing Zhu, Jiangchao Yao, Bo Han +6
In ordinary distillation, student networks are trained with soft labels (SLs) given by pretrained teacher networks, and students are expected to improve upon teachers since SLs are…
TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning
Lu Wang, Xiaofu Chang, Shuang Li +7
Dynamic graph modeling has recently attracted much attention due to its extensive applications in many real-world scenarios, such as recommendation systems, financial transactions,…
M6-T: Exploring Sparse Expert Models and Beyond
An Yang, Junyang Lin, Rui Men +12
Mixture-of-Experts (MoE) models can achieve promising results with outrageous large amount of parameters but constant computation cost, and thus it has become a trend in model scal…
Contrastive Attraction and Contrastive Repulsion for Representation Learning
Huangjie Zheng, Xu Chen, Jiangchao Yao +7
Contrastive learning (CL) methods effectively learn data representations in a self-supervision manner, where the encoder contrasts each positive sample over multiple negative sampl…
Device-Cloud Collaborative Learning for Recommendation
Jiangchao Yao, Feng Wang, KunYang Jia +3
With the rapid development of storage and computing power on mobile devices, it becomes critical and popular to deploy models on devices to save onerous communication latencies and…
Inductive Granger Causal Modeling for Multivariate Time Series
Yunfei Chu, Xiaowei Wang, Jianxin Ma +3
Granger causal modeling is an emerging topic that can uncover Granger causal relationship behind multivariate time series data. In many real-world systems, it is common to encounte…
Efficient and Scalable Structure Learning for Bayesian Networks: Algorithms and Applications
Rong Zhu, Andreas Pfadler, Ziniu Wu +6
Structure Learning for Bayesian network (BN) is an important problem with extensive research. It plays central roles in a wide variety of applications in Alibaba Group. However, ex…
FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data
Yuexiang Xie, Zhen Wang, Yaliang Li +6
High-order interactive features capture the correlation between different columns and thus are promising to enhance various learning tasks on ubiquitous tabular data. To automate t…
Understanding Negative Sampling in Graph Representation Learning
Zhen Yang, Ming Ding, Chang Zhou +3
Graph representation learning has been extensively studied in recent years. Despite its potential in generating continuous embeddings for various networks, both the effectiveness a…
Bayes EMbedding (BEM): Refining Representation by Integrating Knowledge Graphs and Behavior-specific Networks
Yuting Ye, Xuwu Wang, Jiangchao Yao +4
Low-dimensional embeddings of knowledge graphs and behavior graphs have proved remarkably powerful in varieties of tasks, from predicting unobserved edges between entities to conte…