Feng Niu
Artificial Intelligence · 2011–2012
A researcher in the Science corpus: 5 papers; trusts 17 researchers and is trusted by 147 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 36 citations into it; #1,891 of 35,345 by trust here, #2,198 by citations
- Natural Language Processing — 14 citations into it; #4,991 of 5,688 by trust here, #4,983 by citations
- Optimization & Training — 14 citations into it; #292 of 2,601 by trust here, #214 by citations
- Probabilistic & Bayesian Methods — 12 citations into it
- Computer Networks and Communications — 11 citations into it
- Management Science and Operations Research — 9 citations into it
- Information Systems — 6 citations into it; #6,121 of 8,771 by trust here, #5,303 by citations
Whom Feng Niu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Dimitri P. Bertsekas
- Pedro M. Domingos
- Matthew Richardson
- Robert Tibshirani
- Fei Wu
- Mike D. Mintz
- Hoifung Poon
- Andrew McCallum
- John D. Lafferty
- Jun Zhu
Who trusts Feng Niu overall
- Fabio Aiolli
- Christopher De Sa
- Yuke Zhu
- Ce Zhang
- Christopher R'e
- Aäron van den Oord
- Xinxi Wang
- Li Fei-Fei
- Maximilian Nickel
- Yang Song
Papers from Feng Niu's perspective
Ordered by how strongly the researchers Feng Niu trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent (2011) — 2,372 citations
- Tuffy: Scaling up Statistical Inference in Markov Logic Networks using an RDBMS (2011) — 306 citations
- Elementary: Large-Scale Knowledge-Base Construction via Machine Learning and Statistical Inference (2012) — 101 citations
- Million Song Dataset Challenge ! (2012)
- DeepDive: Web-scale Knowledge-base Construction using Statistical Learning and Inference (2012) — 214 citations