791 citations · 1.5k across the 57 of their papers we have counts for
35 papers · 1 filter
Domain-Guided Conditional Diffusion Model for Unsupervised Domain Adaptation
Yulong Zhang, Shuhao Chen, Weisen Jiang +3
Limited transferability hinders the performance of deep learning models when applied to new application scenarios. Recently, Unsupervised Domain Adaptation (UDA) has achieved signi…
Nonparametric Iterative Machine Teaching
Chen Zhang, Xiaofeng Cao, Weiyang Liu +2
In this paper, we consider the problem of Iterative Machine Teaching (IMT), where the teacher provides examples to the learner iteratively such that the learner can achieve fast co…
A Survey on Time-Series Pre-Trained Models
Qianli Ma, Zhen Liu, Zhenjing Zheng +4
Time-Series Mining (TSM) is an important research area since it shows great potential in practical applications. Deep learning models that rely on massive labeled data have been ut…
A Survey of Learning on Small Data: Generalization, Optimization, and Challenge
Xiaofeng Cao, Weixin Bu, Shengjun Huang +4
Learning on big data brings success for artificial intelligence (AI), but the annotation and training costs are expensive. In future, learning on small data that approximates the g…
Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization
Quanming Yao, Yaqing Wang, Bo Han +1
Nonconvex regularization has been popularly used in low-rank matrix learning. However, extending it for low-rank tensor learning is still computationally expensive. To address this…
Revisiting Over-smoothing in BERT from the Perspective of Graph
Han Shi, Jiahui Gao, Hang Xu +5
Recently over-smoothing phenomenon of Transformer-based models is observed in both vision and language fields. However, no existing work has delved deeper to further investigate th…
Dropout's Dream Land: Generalization from Learned Simulators to Reality
Zac Wellmer, James T. Kwok
A World Model is a generative model used to simulate an environment. World Models have proven capable of learning spatial and temporal representations of Reinforcement Learning env…
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation
Haoang Chi, Feng Liu, Wenjing Yang +5
In few-shot domain adaptation (FDA), classifiers for the target domain are trained with accessible labeled data in the source domain (SD) and few labeled data in the target domain…
SparseBERT: Rethinking the Importance Analysis in Self-attention
Han Shi, Jiahui Gao, Xiaozhe Ren +4
Transformer-based models are popularly used in natural language processing (NLP). Its core component, self-attention, has aroused widespread interest. To understand the self-attent…
Topology-aware Tensor Decomposition for Meta-graph Learning
Hansi Yang, Peiyu Zhang, Quanming Yao
Heterogeneous graphs generally refers to graphs with different types of nodes and edges. A common approach for extracting useful information from heterogeneous graphs is to use met…
A Survey of Label-noise Representation Learning: Past, Present and Future
Bo Han, Quanming Yao, Tongliang Liu +4
Classical machine learning implicitly assumes that labels of the training data are sampled from a clean distribution, which can be too restrictive for real-world scenarios. However…
A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Completion
Yaqing Wang, Quanming Yao, James T. Kwok
Matrix learning is at the core of many machine learning problems. A number of real-world applications such as collaborative filtering and text mining can be formulated as a low-ran…
Effective Decoding in Graph Auto-Encoder using Triadic Closure
Han Shi, Haozheng Fan, James T. Kwok
The (variational) graph auto-encoder and its variants have been popularly used for representation learning on graph-structured data. While the encoder is often a powerful graph con…
Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS
Han Shi, Renjie Pi, Hang Xu +3
Neural Architecture Search (NAS) has shown great potentials in finding better neural network designs. Sample-based NAS is the most reliable approach which aims at exploring the sea…
Policy Prediction Network: Model-Free Behavior Policy with Model-Based Learning in Continuous Action Space
Zac Wellmer, James Kwok
This paper proposes a novel deep reinforcement learning architecture that was inspired by previous tree structured architectures which were only useable in discrete action spaces.…
Efficient Neural Interaction Function Search for Collaborative Filtering
Quanming Yao, Xiangning Chen, James Kwok +2
In collaborative filtering (CF), interaction function (IFC) plays the important role of capturing interactions among items and users. The most popular IFC is the inner product, whi…
Blockwise Adaptivity: Faster Training and Better Generalization in Deep Learning
Shuai Zheng, James T. Kwok
Stochastic methods with coordinate-wise adaptive stepsize (such as RMSprop and Adam) have been widely used in training deep neural networks. Despite their fast convergence, they ca…
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback
Shuai Zheng, Ziyue Huang, James T. Kwok
Communication overhead is a major bottleneck hampering the scalability of distributed machine learning systems. Recently, there has been a surge of interest in using gradient compr…
Efficient Low-Rank Semidefinite Programming with Robust Loss Functions
Quanming Yao, Hangsi Yang, En-Liang Hu +1
In real-world applications, it is important for machine learning algorithms to be robust against data outliers or corruptions. In this paper, we focus on improving the robustness o…
Generalizing from a Few Examples: A Survey on Few-Shot Learning
Yaqing Wang, Quanming Yao, James Kwok +1
Machine learning has been highly successful in data-intensive applications but is often hampered when the data set is small. Recently, Few-Shot Learning (FSL) is proposed to tackle…
General Convolutional Sparse Coding with Unknown Noise
Yaqing Wang, James T. Kwok, Lionel M. Ni
Convolutional sparse coding (CSC) can learn representative shift-invariant patterns from multiple kinds of data. However, existing CSC methods can only model noises from Gaussian d…
FasTer: Fast Tensor Completion with Nonconvex Regularization
Quanming Yao, James T Kwok, Bo Han
Low-rank tensor completion problem aims to recover a tensor from limited observations, which has many real-world applications. Due to the easy optimization, the convex overlapping…
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data
Shuai Zheng, James T. Kwok
Variance reduction has been commonly used in stochastic optimization. It relies crucially on the assumption that the data set is finite. However, when the data are imputed with ran…
Power Law in Sparsified Deep Neural Networks
Lu Hou, James T. Kwok
The power law has been observed in the degree distributions of many biological neural networks. Sparse deep neural networks, which learn an economical representation from the data,…