40 citations · 126 across the 15 of their papers we have counts for
15 papers
Accurate and interpretable drug-drug interaction prediction enabled by knowledge subgraph learning
Yaqing Wang, Zaifei Yang, Quanming Yao
Background: Discovering potential drug-drug interactions (DDIs) is a long-standing challenge in clinical treatments and drug developments. Recently, deep learning techniques have b…
PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction
Shiguang Wu, Yaqing Wang, Quanming Yao
Molecular property prediction (MPP) plays a crucial role in biomedical applications, but it often encounters challenges due to a scarcity of labeled data. Existing works commonly a…
AdaDiff: Accelerating Diffusion Models through Step-Wise Adaptive Computation
Shengkun Tang, Yaqing Wang, Caiwen Ding +3
Diffusion models achieve great success in generating diverse and high-fidelity images, yet their widespread application, especially in real-time scenarios, is hampered by their inh…
Teach LLMs to Personalize -- An Approach inspired by Writing Education
Cheng Li, Mingyang Zhang, Qiaozhu Mei +4
Personalized text generation is an emerging research area that has attracted much attention in recent years. Most studies in this direction focus on a particular domain by designin…
Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering
Yan Wen, Chen Gao, Lingling Yi +3
Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner. However, existing works either…
ColdNAS: Search to Modulate for User Cold-Start Recommendation
Shiguang Wu, Yaqing Wang, Qinghe Jing +3
Making personalized recommendation for cold-start users, who only have a few interaction histories, is a challenging problem in recommendation systems. Recent works leverage hypern…
Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement
Yan Li, Xinjiang Lu, Yaqing Wang +1
Time series forecasting has been a widely explored task of great importance in many applications. However, it is common that real-world time series data are recorded in a short tim…
AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning
Yaqing Wang, Sahaj Agarwal, Subhabrata Mukherjee +4
Standard fine-tuning of large pre-trained language models (PLMs) for downstream tasks requires updating hundreds of millions to billions of parameters, and storing a large copy of…
You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model
Shengkun Tang, Yaqing Wang, Zhenglun Kong +6
Large-scale Transformer models bring significant improvements for various downstream vision language tasks with a unified architecture. The performance improvements come with incre…
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…
AdaMix: Mixture-of-Adaptations for Parameter-efficient Model Tuning
Yaqing Wang, Sahaj Agarwal, Subhabrata Mukherjee +4
Standard fine-tuning of large pre-trained language models (PLMs) for downstream tasks requires updating hundreds of millions to billions of parameters, and storing a large copy of…
LiST: Lite Prompted Self-training Makes Parameter-Efficient Few-shot Learners
Yaqing Wang, Subhabrata Mukherjee, Xiaodong Liu +3
We present a new method LiST is short for Lite Prompted Self-Training for parameter-efficient fine-tuning of large pre-trained language models (PLMs) for few-shot learning. LiST im…
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…
Online Convolutional Sparse Coding with Sample-Dependent Dictionary
Yaqing Wang, Quanming Yao, James T. Kwok +1
Convolutional sparse coding (CSC) has been popularly used for the learning of shift-invariant dictionaries in image and signal processing. However, existing methods have limited sc…
Scalable Online Convolutional Sparse Coding
Yaqing Wang, Quanming Yao, James T. Kwok +1
Convolutional sparse coding (CSC) improves sparse coding by learning a shift-invariant dictionary from the data. However, existing CSC algorithms operate in the batch mode and are…