Shuming Ma

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 20 papers; trusts 739 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 582 citations into it; #12,702 of 35,345 by trust here, #3,046 by citations
• Natural Language Processing — 343 citations into it
• Computer Vision and Pattern Recognition — 318 citations into it; #12,807 of 21,575 by trust here, #2,949 by citations
• Evaluation & Benchmarks — 169 citations into it
• Large Language Models — 161 citations into it; #469 of 799 by trust here, #419 by citations
• Model Architectures — 106 citations into it; #1,568 of 2,278 by trust here, #1,070 by citations
• Multimodal Learning — 91 citations into it
• Representation & Self-Supervised Learning — 50 citations into it

Whom Shuming Ma trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Alex Krizhevsky
6. Jimmy Ba
7. Kaiming He
8. Yann LeCun
9. Alex Graves
10. Alec Radford

Who trusts Shuming Ma overall

1. Li Dong
2. Shaohan Huang
3. Zewen Chi
4. Katikapalli Subramanyam Kalyan
5. Wenhui Wang
6. Jiayu Ding
7. Hongyu Wang
8. Yaru Hao
9. Cheng-Ping Hsieh
10. Furu Wei

Papers from Shuming Ma's perspective

Ordered by how strongly the researchers Shuming Ma 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. ImageNet: A large-scale hierarchical image database
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
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

• Language Is Not All You Need: Aligning Perception with Language Models (2023) — 786 citations
• Kosmos-2: Grounding Multimodal Large Language Models to the World (2023) — 1,249 citations
• Language Models are General-Purpose Interfaces (2022) — 113 citations
• On the Representation Collapse of Sparse Mixture of Experts (2022) — 199 citations
• DeepNet: Scaling Transformers to 1,000 Layers (2022) — 261 citations
• Alternating Language Modeling for Cross-Lingual Pre-Training (2020) — 94 citations
• meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting (2017) — 171 citations
• Foundation Transformers (2022) — 30 citations
• DeltaLM: Encoder-Decoder Pre-training for Language Generation and Translation by Augmenting Pretrained Multilingual Encoders (2021) — 92 citations
• LongNet: Scaling Transformers to 1, 000, 000, 000 Tokens (2023) — 265 citations
• A Length-Extrapolatable Transformer (2022) — 176 citations
• A whole-slide foundation model for digital pathology from real-world data (2024) — 934 citations
• The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits (2024) — 424 citations
• Retentive Network: A Successor to Transformer for Large Language Models (2023) — 719 citations
• TorchScale: Transformers at Scale (2022) — 12 citations
• mT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs (2021) — 82 citations
• BlonDe: An Automatic Evaluation Metric for Document-level Machine Translation (2021) — 75 citations
• XLM-E: Cross-lingual Language Model Pre-training via ELECTRA (2021) — 139 citations
• Global Encoding for Abstractive Summarization (2018) — 153 citations
• SGM: Sequence Generation Model for Multi-label Classification (2018) — 434 citations