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Huiyuan Li

7 accepted papers

2026

AirWino: Optimized Winograd Convolution for Accelerating CNN Inference on ARMv8 Processors

AAAI 2026technical

As Convolutional Neural Networks (CNNs) continue to gain traction in deep learning, Winograd convolution has emerged as a key algorithm to enhance computational efficiency. Although ARM-based CPUs are increasingly prevalent in mobile devices, embedded systems and HPC servers, existing 2D Winograd co

Cited by 0SourcePDFScholar
2026

Factorization-in-Loop:Proximal Fill-in Minimization for Sparse Matrix Reordering

AAAI 2026technical

Fill-ins are new nonzero elements in the summation of the upper and lower triangular factors generated during LU factorization. For large sparse matrices, they will increase the memory usage and computational time, and be reduced through proper row or column arrangement, namely matrix reordering. Fi

Cited by 0SourcePDFScholar
2022

Denoising-Guided Deep Reinforcement Learning For Social Recommendation

ICASSP 2022accepted

Social recommendation (SR) aims to enhance the performance of recommendations by incorporating social information. However, such information is not always reliable, e.g., some of the friends may share similar preferences with the user on a specific item, while others may be irrelevant to this item d…

Cited by 0SourceScholar
2022

Denoising-Oriented Deep Hierarchical Reinforcement Learning for Next-Basket Recommendation⋆

ICASSP 2022accepted

Next basket recommendation aims to provide users a basket of items on the next visit by considering the sequence of their historical baskets. However, since a user’s purchase interests vary over time, historical baskets often contain many irrelevant items to his/her next choices. Therefore, it is ne…

Cited by 0SourceScholar
2022

MTAF: Shopping Guide Micro-Videos Popularity Prediction Using Multimodal and Temporal Attention Fusion Approach

ICASSP 2022accepted

Predicting the popularity of shopping guide micro-videos incorporating merchandise is crucial for online advertising. What are the significant factors affecting the popularity of the micro-video? How to extract and effectively fuse multiple modalities for the micro-video popularity prediction? This…

Cited by 0SourceScholar
2021

Co-Capsule Networks Based Knowledge Transfer for Cross-Domain Recommendation

ICASSP 2021accepted

Cross-domain recommendation (CDR) technology is proved to be an effective way to tackle the difficulties encountered by traditional recommender technology (e.g. CF), such as data sparsity and cold-start. However, on account of the heterogeneity, it is difficult to enhance the representation of user…

Cited by 0SourceScholar