21 citations · 43 across the 9 of their papers we have counts for
9 papers
RAPO: An Adaptive Ranking Paradigm for Bilingual Lexicon Induction
Zhoujin Tian, Chaozhuo Li, Shuo Ren +8
Bilingual lexicon induction induces the word translations by aligning independently trained word embeddings in two languages. Existing approaches generally focus on minimizing the…
Test-Time Training for Graph Neural Networks
Yiqi Wang, Chaozhuo Li, Wei Jin +4
Graph Neural Networks (GNNs) have made tremendous progress in the graph classification task. However, a performance gap between the training set and the test set has often been not…
Going Deeper into Permutation-Sensitive Graph Neural Networks
Zhongyu Huang, Yingheng Wang, Chaozhuo Li +1
The invariance to permutations of the adjacency matrix, i.e., graph isomorphism, is an overarching requirement for Graph Neural Networks (GNNs). Conventionally, this prerequisite c…
Ada-Ranker: A Data Distribution Adaptive Ranking Paradigm for Sequential Recommendation
Xinyan Fan, Jianxun Lian, Wayne Xin Zhao +3
A large-scale recommender system usually consists of recall and ranking modules. The goal of ranking modules (aka rankers) is to elaborately discriminate users' preference on item…
Progressively Optimized Bi-Granular Document Representation for Scalable Embedding Based Retrieval
Shitao Xiao, Zheng Liu, Weihao Han +9
Ad-hoc search calls for the selection of appropriate answers from a massive-scale corpus. Nowadays, the embedding-based retrieval (EBR) becomes a promising solution, where deep lea…
Gophormer: Ego-Graph Transformer for Node Classification
Jianan Zhao, Chaozhuo Li, Qianlong Wen +5
Transformers have achieved remarkable performance in a myriad of fields including natural language processing and computer vision. However, when it comes to the graph mining area,…
AdsGNN: Behavior-Graph Augmented Relevance Modeling in Sponsored Search
Chaozhuo Li, Bochen Pang, Yuming Liu +7
Sponsored search ads appear next to search results when people look for products and services on search engines. In recent years, they have become one of the most lucrative channel…
Learning with Noisy Labels for Sentence-level Sentiment Classification
Hao Wang, Bing Liu, Chaozhuo Li +2
Deep neural networks (DNNs) can fit (or even over-fit) the training data very well. If a DNN model is trained using data with noisy labels and tested on data with clean labels, the…
Multi-Hot Compact Network Embedding
Chaozhuo Li, Senzhang Wang, Philip S. Yu +1
Network embedding, as a promising way of the network representation learning, is capable of supporting various subsequent network mining and analysis tasks, and has attracted growi…