Jianmo Ni
Artificial Intelligence · 2018–2022
A researcher in the Science corpus: 10 papers; trusts 161 researchers and is trusted by 25 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 333 citations into it; #8,846 of 35,345 by trust here, #3,423 by citations
- Natural Language Processing — 222 citations into it; #2,280 of 5,688 by trust here, #1,316 by citations
- Computer Vision and Pattern Recognition — 128 citations into it
- Evaluation & Benchmarks — 91 citations into it
- Information Retrieval & Search — 79 citations into it; #819 of 1,022 by trust here, #277 by citations
- Large Language Models — 72 citations into it
- Model Architectures — 29 citations into it
- Information Systems — 24 citations into it; #6,415 of 8,771 by trust here, #5,078 by citations
Whom Jianmo Ni trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Ilya Sutskever
- Geoffrey E. Hinton
- Yoshua Bengio
- Noam Shazeer
- Sepp Hochreiter
- Alex Krizhevsky
- Kyunghyun Cho
- Alec Radford
- Jacob Devlin
Who trusts Jianmo Ni overall
- Wayne Xin Zhao
- Shitao Xiao
- Liang Wang
- Zhuyun Dai
- Luyu Gao
- Niklas Muennighoff
- Zeqiu Wu
- Hong-Liu Cao
- Oshin Agarwal
- Yupeng Hou
Papers from Jianmo Ni's perspective
Ordered by how strongly the researchers Jianmo Ni 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- 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
- Gradient-based learning applied to document recognition
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- ExT5: Towards Extreme Multi-Task Scaling for Transfer Learning (2021) — 240 citations
- Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects (2019) — 1,798 citations
- Large Dual Encoders Are Generalizable Retrievers (2021) — 644 citations
- Transformer Memory as a Differentiable Search Index (2022) — 444 citations
- Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models (2021) — 830 citations
- Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering (2018) — 38 citations
- LongT5: Efficient Text-To-Text Transformer for Long Sequences (2021) — 414 citations
- Promptagator: Few-shot Dense Retrieval From 8 Examples (2022) — 350 citations
- Scaling Up Models and Data with t5x and seqio (2022) — 224 citations
- RankT5: Fine-Tuning T5 for Text Ranking with Ranking Losses (2022)