Xikun Zhang
Artificial Intelligence · with Leskovec · 2019–2022
A researcher in the Science corpus: 6 papers; trusts 65 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 135 citations into it; #20,172 of 35,345 by trust here, #2,359 by citations
- Natural Language Processing — 71 citations into it
- Computer Vision and Pattern Recognition — 34 citations into it
- Large Language Models — 29 citations into it
- Molecular Biology — 20 citations into it
- Knowledge Graphs & Representation — 18 citations into it
- Graph Learning — 17 citations into it
- Information Systems — 10 citations into it; #4,707 of 8,771 by trust here, #7,705 by citations
Whom Xikun Zhang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jacob Devlin
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Ilya Sutskever
- Aäron van den Oord
- Alex Krizhevsky
- Yann LeCun
- Kaiming He
- Alex Graves
Who trusts Xikun Zhang overall
- Rodrigo Nogueira
- Woojeong Jin
- Avijit Thawani
- Bowen Jin
- Linhao Luo
- Joakim Skarding
- Jimmy J. Li
- Yanbang Wang
- Pedro A. Szekely
- Emanuele Rossi
Papers from Xikun Zhang's perspective
Ordered by how strongly the researchers Xikun Zhang 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)
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- 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
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- On the Opportunities and Risks of Foundation Models (2021) — 6,923 citations
- Deep Bidirectional Language-Knowledge Graph Pretraining (2022) — 279 citations
- GreaseLM: Graph REASoning Enhanced Language Models for Question Answering (2022) — 306 citations
- Do Language Embeddings capture Scales? (2020) — 8 citations
- Do Language Embeddings capture Scales? (2020) — 100 citations
- Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks (2019) — 1,029 citations