Fandong Meng

Artificial Intelligence · 2015–2023

A researcher in the Science corpus: 10 papers; trusts 260 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 240 citations into it; #30,516 of 35,345 by trust here, #7,172 by citations
• Natural Language Processing — 164 citations into it
• Computer Vision and Pattern Recognition — 92 citations into it
• Model Architectures — 49 citations into it
• Evaluation & Benchmarks — 36 citations into it
• Large Language Models — 27 citations into it
• Optimization & Training — 18 citations into it
• Management Science and Operations Research — 8 citations into it

Whom Fandong Meng trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Diederik P. Kingma
5. Franz Joseph Och
6. Kyunghyun Cho
7. Alex Graves
8. Yann LeCun
9. Dzmitry Bahdanau
10. Alex Krizhevsky

Who trusts Fandong Meng overall

1. Jonas Gehring
2. Lukasz Kaiser
3. Duyu Tang
4. Jiwei Li
5. Zhe Gan
6. Michael Auli
7. Zi-Yi Dou
8. Biao Zhang
9. Hao Zhou
10. Ashish Vaswani

Papers from Fandong Meng's perspective

Ordered by how strongly the researchers Fandong Meng trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• Encoding Source Language with Convolutional Neural Network for Machine Translation (2015) — 109 citations
• Bridging the Gap between Training and Inference for Neural Machine Translation (2019) — 276 citations
• Large Language Models Are Not Robust Multiple Choice Selectors (2023) — 481 citations
• Towards Robust Neural Machine Translation (2018) — 167 citations
• GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling (2019) — 97 citations
• Is ChatGPT a Good NLG Evaluator? A Preliminary Study (2023) — 643 citations
• An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-Tuning (2023) — 725 citations
• Token-level Adaptive Training for Neural Machine Translation (2020) — 39 citations
• Explicitly Modeling Adaptive Depths for Transformer (2020)
• A Deep Memory-based Architecture for Sequence-to-Sequence Learning (2015) — 20 citations