Hongyu Wang
Artificial Intelligence · with Ma · 2022–2024
A researcher in the Science corpus: 4 papers; trusts 46 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 97 citations into it
- Computer Vision and Pattern Recognition — 51 citations into it; #21,569 of 21,575 by trust here, #12,689 by citations
- Natural Language Processing — 47 citations into it
- Evaluation & Benchmarks — 25 citations into it
- Optimization & Training — 16 citations into it
Whom Hongyu Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Yoshua Bengio
- Geoffrey E. Hinton
- Kaiming He
- Alex Krizhevsky
- Ilya Sutskever
- Jimmy Ba
- Yann LeCun
- Alec Radford
- Xavier Glorot
Who trusts Hongyu Wang overall
- Shaohan Huang
- Shuming Ma
- Li Dong
- Yaru Hao
- Wenhui Wang
- Manoj Kumar
- Sanyuan Chen
- Shuangfei Zhai
- Aohan Zeng
- Yuxin Fang
Papers from Hongyu Wang's perspective
Ordered by how strongly the researchers Hongyu Wang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Gradient-based learning applied to document recognition
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- DeepNet: Scaling Transformers to 1,000 Layers (2022) — 261 citations
- Foundation Transformers (2022) — 30 citations
- The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits (2024) — 424 citations
- TorchScale: Transformers at Scale (2022) — 12 citations