Jianxin Ma

Artificial Intelligence · with Zhou · 2019–2024

A researcher in the Science corpus: 9 papers; trusts 521 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 363 citations into it
• Computer Vision and Pattern Recognition — 278 citations into it; #21,143 of 21,575 by trust here, #4,286 by citations
• Large Language Models — 126 citations into it
• Multimodal Learning — 95 citations into it
• Natural Language Processing — 85 citations into it
• Generative Models — 82 citations into it
• Representation & Self-Supervised Learning — 59 citations into it
• Model Architectures — 48 citations into it

Whom Jianxin 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. Alex Krizhevsky
5. Kaiming He
6. Ilya Sutskever
7. Yann LeCun
8. Jimmy Ba
9. Ruslan Salakhutdinov
10. Alex Graves

Who trusts Jianxin Ma overall

1. Xiang Wang
2. Yiding Yang
3. Xiangnan He
4. Yin-wei Wei
5. Junliang Yu
6. Yu Zheng
7. Wenjie Wang
8. Hongye Jin
9. Yukuo Cen
10. Zhe Xie

Papers from Jianxin Ma's perspective

Ordered by how strongly the researchers Jianxin 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. 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework (2022) — 1,080 citations
• M6-UFC: Unifying Multi-Modal Controls for Conditional Image Synthesis via Non-Autoregressive Generative Transformers (2021) — 49 citations
• M6: A Chinese Multimodal Pretrainer (2021) — 152 citations
• Qwen Technical Report (2023) — 4,095 citations
• Qwen2 Technical Report (2024) — 2,333 citations
• Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework (2022) — 252 citations
• UFC-BERT: Unifying Multi-Modal Controls for Conditional Image Synthesis (2021) — 43 citations
• Disentangled Graph Convolutional Networks (2019) — 377 citations
• Learning Disentangled Representations for Recommendation (2019) — 399 citations