27 citations · 57 across the 8 of their papers we have counts for
8 papers
Expression Syntax Information Bottleneck for Math Word Problems
Jing Xiong, Chengming Li, Min Yang +2
Math Word Problems (MWP) aims to automatically solve mathematical questions given in texts. Previous studies tend to design complex models to capture additional information in the…
Self-Distillation with Meta Learning for Knowledge Graph Completion
Yunshui Li, Junhao Liu, Chengming Li +1
In this paper, we propose a selfdistillation framework with meta learning(MetaSD) for knowledge graph completion with dynamic pruning, which aims to learn compressed graph embeddin…
Self-consistent Reasoning For Solving Math Word Problems
Jing Xiong, Zhongwei Wan, Xiping Hu +2
Math word problems (MWPs) is a task that automatically derives solution expression from a giving math problems in text. The previous studies suffer from spurious correlations betwe…
Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems
Guanghu Yuan, Fajie Yuan, Yudong Li +9
Existing benchmark datasets for recommender systems (RS) either are created at a small scale or involve very limited forms of user feedback. RS models evaluated on such datasets of…
Enhancing Top-N Item Recommendations by Peer Collaboration
Yang Sun, Fajie Yuan, Min Yang +3
Deep neural networks (DNN) have achieved great success in the recommender systems (RS) domain. However, to achieve remarkable performance, DNN-based recommender models often requir…
Scene-adaptive Knowledge Distillation for Sequential Recommendation via Differentiable Architecture Search
Lei Chen, Fajie Yuan, Jiaxi Yang +2
Sequential recommender systems (SRS) have become a research hotspot due to its power in modeling user dynamic interests and sequential behavioral patterns. To maximize model expres…
User-specific Adaptive Fine-tuning for Cross-domain Recommendations
Lei Chen, Fajie Yuan, Jiaxi Yang +3
Making accurate recommendations for cold-start users has been a longstanding and critical challenge for recommender systems (RS). Cross-domain recommendations (CDR) offer a solutio…
StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking
Jiachun Wang, Fajie Yuan, Jian Chen +4
Deep learning has brought great progress for the sequential recommendation (SR) tasks. With advanced network architectures, sequential recommender models can be stacked with many h…