Defu Lian
Artificial Intelligence · 2014–2024
A researcher in the Science corpus: 8 papers; trusts 63 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 278 citations into it; #10,798 of 35,345 by trust here, #4,063 by citations
- Natural Language Processing — 95 citations into it
- Information Systems — 94 citations into it; #1,814 of 8,771 by trust here, #924 by citations
- Computer Vision and Pattern Recognition — 93 citations into it
- Evaluation & Benchmarks — 45 citations into it
- Information Retrieval & Search — 36 citations into it
- Signal Processing — 32 citations into it
- Representation & Self-Supervised Learning — 28 citations into it
Whom Defu Lian trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Chih-Jen Lin
- David Ruppert
- Yehuda Koren
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Gediminas Adomavicius
- Alex Krizhevsky
- Ilya Sutskever
- Yann LeCun
Who trusts Defu Lian overall
- Xiangnan He
- Fuzheng Zhang
- Hongwei Wang
- Jia-Dong Zhang
- Xutao Li
- Xiang Wang
- Qingyao Ai
- Xing Xie
- Chaochao Chen
- Yongfeng Zhang
Papers from Defu Lian's perspective
Ordered by how strongly the researchers Defu Lian trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- ImageNet: A large-scale hierarchical image database
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
Their papers
- Collaborative Knowledge Base Embedding for Recommender Systems (2016) — 1,809 citations
- M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation (2024) — 1,560 citations
- Understanding the planning of LLM agents: A survey (2024) — 483 citations
- When large language models meet personalization: perspectives of challenges and opportunities (2023) — 347 citations
- Frequency-domain MLPs are More Effective Learners in Time Series Forecasting (2023) — 474 citations
- C-Pack: Packed Resources For General Chinese Embeddings (2023) — 720 citations
- Investigating Data Variance in Evaluations of Automatic Machine Translation Metrics (2022) — 5 citations
- GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation (2014) — 657 citations