Yu Meng

Artificial Intelligence · Neural Information Processing Systems · 2018–2024

A researcher in the Science corpus: 9 papers; trusts 430 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 387 citations into it; #20,853 of 35,345 by trust here, #11,921 by citations
• Natural Language Processing — 218 citations into it; #4,281 of 5,688 by trust here, #3,190 by citations
• Large Language Models — 128 citations into it; #749 of 799 by trust here, #663 by citations
• Computer Vision and Pattern Recognition — 127 citations into it
• Prompting & Reasoning — 65 citations into it
• Evaluation & Benchmarks — 62 citations into it
• Representation & Self-Supervised Learning — 46 citations into it
• Alignment & RLHF — 33 citations into it

Whom Yu Meng trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ilya Sutskever
4. Diederik P. Kingma
5. Alex Graves
6. Alex Krizhevsky
7. Yann LeCun
8. Kaiming He
9. Tomas Mikolov
10. Richard Socher

Who trusts Yu Meng overall

1. Jiaxin Huang
2. Jiawei Han
3. Shervin Minaee
4. Patrick M. Haluptzok
5. Yue Yu
6. Bill Yuchen Lin
7. Bowen Jin
8. Bowen Jin
9. Guohao Li
10. Tianyu Gao

Papers from Yu Meng's perspective

Ordered by how strongly the researchers Yu 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining (2021) — 227 citations
• Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning (2022) — 65 citations
• Weakly-Supervised Neural Text Classification (2018) — 200 citations
• SimPO: Simple Preference Optimization with a Reference-Free Reward (2024) — 1,086 citations
• Evaluating Large Language Models at Evaluating Instruction Following (2023) — 335 citations
• Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias (2023) — 367 citations
• Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators (2022) — 17 citations
• Generating Training Data with Language Models: Towards Zero-Shot Language Understanding (2022) — 302 citations
• Weakly-Supervised Hierarchical Text Classification (2018) — 141 citations