86 citations · 223 across the 10 of their papers we have counts for
10 papers
Understanding and Optimizing Agentic Workflows via Shapley value
Yingxuan Yang, Bo Huang, Siyuan Qi +14
Agentic workflows have become the dominant paradigm for building complex AI systems, orchestrating specialized components, such as planning, reasoning, action execution, and reflec…
Meta-Learning Triplet Network with Adaptive Margins for Few-Shot Named Entity Recognition
Chengcheng Han, Renyu Zhu, Jun Kuang +5
Meta-learning methods have been widely used in few-shot named entity recognition (NER), especially prototype-based methods. However, the Other(O) class is difficult to be represent…
FFHR: Fully and Flexible Hyperbolic Representation for Knowledge Graph Completion
Wentao Shi, Junkang Wu, Xuezhi Cao +4
Learning hyperbolic embeddings for knowledge graph (KG) has gained increasing attention due to its superiority in capturing hierarchies. However, some important operations in hyper…
Adap-: Adaptively Modulating Embedding Magnitude for Recommendation
Jiawei Chen, Junkang Wu, Jiancan Wu +3
Recent years have witnessed the great successes of embedding-based methods in recommender systems. Despite their decent performance, we argue one potential limitation of these meth…
Long-Tailed Class Incremental Learning
Xialei Liu, Yu-Song Hu, Xu-Sheng Cao +3
In class incremental learning (CIL) a model must learn new classes in a sequential manner without forgetting old ones. However, conventional CIL methods consider a balanced distrib…
Popularity Bias Is Not Always Evil: Disentangling Benign and Harmful Bias for Recommendation
Zihao Zhao, Jiawei Chen, Sheng Zhou +4
Recommender system usually suffers from severe popularity bias -- the collected interaction data usually exhibits quite imbalanced or even long-tailed distribution over items. Such…
DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network
Junkang Wu, Wentao Shi, Xuezhi Cao +5
Knowledge graph completion (KGC) has become a focus of attention across deep learning community owing to its excellent contribution to numerous downstream tasks. Although recently…
A Machine Learning Approach To Prevent Malicious Calls Over Telephony Networks
Huichen Li, Xiaojun Xu, Chang Liu +6
Malicious calls, i.e., telephony spams and scams, have been a long-standing challenging issue that causes billions of dollars of annual financial loss worldwide. This work presents…
Collaborative Filtering with Graph-based Implicit Feedback
Minzhe Niu, Weinan Zhang, Yanru Qu +4
Introducing consumed items as users' implicit feedback in matrix factorization (MF) method, SVD++ is one of the most effective collaborative filtering methods for personalized reco…
Revealing Multiple Layers of Hidden Community Structure in Networks
Kun He, Sucheta Soundarajan, Xuezhi Cao +2
We introduce a new conception of community structure, which we refer to as hidden community structure. Hidden community structure refers to a specific type of overlapping community…