Jheng-Hong Yang

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 7 papers; trusts 113 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 113 citations into it; #6,019 of 35,345 by trust here, #7,657 by citations
• Information Retrieval & Search — 86 citations into it; #315 of 1,022 by trust here, #248 by citations
• Computer Vision and Pattern Recognition — 41 citations into it; #10,768 of 21,575 by trust here, #9,187 by citations
• Natural Language Processing — 41 citations into it
• Large Language Models — 30 citations into it
• Evaluation & Benchmarks — 22 citations into it
• Information Systems — 18 citations into it; #1,854 of 8,771 by trust here, #4,682 by citations
• Representation & Self-Supervised Learning — 10 citations into it

Whom Jheng-Hong Yang trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Rodrigo Nogueira
2. Jimmy J. Lin
3. Geoffrey E. Hinton
4. David R. Cheriton
5. Jacob Devlin
6. Diederik P. Kingma
7. Sebastian Hofstätter
8. Yoshua Bengio
9. Kenton C. T. Lee
10. Kyunghyun Cho

Who trusts Jheng-Hong Yang overall

1. Sebastian Hofstätter
2. Jimmy J. Lin
3. Xueguang Ma
4. Jingtao Zhan
5. Sheng-Chieh Lin
6. Thibault Formal
7. Zhuyun Dai
8. Xinyu Crystina Zhang
9. Mete Sertkan
10. Omar Khattab

Papers from Jheng-Hong Yang's perspective

Ordered by how strongly the researchers Jheng-Hong Yang trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. ImageNet classification with deep convolutional neural networks
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling (2021) — 521 citations
• Pyserini: A Python Toolkit for Reproducible Information Retrieval Research with Sparse and Dense Representations (2021) — 673 citations
• In-Batch Negatives for Knowledge Distillation with Tightly-Coupled Teachers for Dense Retrieval (2021) — 221 citations
• Designing Templates for Eliciting Commonsense Knowledge from Pretrained Sequence-to-Sequence Models (2020) — 7 citations
• Pyserini: An Easy-to-Use Python Toolkit to Support Replicable IR Research with Sparse and Dense Representations (2021) — 105 citations
• Distilling Dense Representations for Ranking using Tightly-Coupled Teachers (2020) — 144 citations
• TTTTTackling WinoGrande Schemas (2020) — 7 citations