10 citations · 16 across the 7 of their papers we have counts for
7 papers
WPGRec: Wavelet Packet Guided Graph Enhanced Sequential Recommendation
Peilin Liu, Zhiquan Ji, Gang Yan
Sequential recommendation aims to model users' evolving interests from noisy and non-stationary interaction streams, where long-term preferences, short-term intents, and localized…
Self-Awareness before Action: Mitigating Logical Inertia via Proactive Cognitive Awareness
Fulong Fan, Peilin Liu, Fengzhe Liu +2
Large language models perform well on many reasoning tasks, yet they often lack awareness of whether their current knowledge or reasoning state is complete. In non-interactive puzz…
Triply Laplacian Scale Mixture Modeling for Seismic Data Noise Suppression
Sirui Pan, Zhiyuan Zha, Shigang Wang +6
Sparsity-based tensor recovery methods have shown great potential in suppressing seismic data noise. These methods exploit tensor sparsity measures capturing the low-dimensional st…
DePRL: Achieving Linear Convergence Speedup in Personalized Decentralized Learning with Shared Representations
Guojun Xiong, Gang Yan, Shiqiang Wang +1
Decentralized learning has emerged as an alternative method to the popular parameter-server framework which suffers from high communication burden, single-point failure and scalabi…
Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates
Guojun Xiong, Gang Yan, Shiqiang Wang +1
With the increasing demand for large-scale training of machine learning models, fully decentralized optimization methods have recently been advocated as alternatives to the popular…
Critical Learning Periods in Federated Learning
Gang Yan, Hao Wang, Jian Li
Federated learning (FL) is a popular technique to train machine learning (ML) models with decentralized data. Extensive works have studied the performance of the global model; howe…
Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers
Guojun Xiong, Gang Yan, Rahul Singh +1
With the increasing demand for large-scale training of machine learning models, consensus-based distributed optimization methods have recently been advocated as alternatives to the…