Haitao Zheng
Artificial Intelligence · with Chen · 2018–2023
A researcher in the Science corpus: 7 papers; trusts 486 researchers and is trusted by 538 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 214 citations into it; #2,437 of 35,345 by trust here, #3,009 by citations
- Computer Vision and Pattern Recognition — 97 citations into it; #1,851 of 21,575 by trust here, #2,381 by citations
- Natural Language Processing — 96 citations into it
- Large Language Models — 60 citations into it
- Efficiency & Compression — 32 citations into it; #72 of 1,264 by trust here, #176 by citations
- Evaluation & Benchmarks — 24 citations into it
- Model Architectures — 24 citations into it
- Optimization & Training — 11 citations into it
Whom Haitao Zheng trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Kaiming He
- Alex Krizhevsky
- Geoffrey E. Hinton
- Yoshua Bengio
- Ilya Sutskever
- Christian Szegedy
- Karen Simonyan
- Yann LeCun
- Diederik P. Kingma
- Ross B. Girshick
Who trusts Haitao Zheng overall
- Jiahui Yu
- Ningning Ma
- Mingxing Tan
- Han Cai
- Yuhui Yuan
- Sachin Mehta
- Xiaoliang Dai
- X. Zhang
- Haokui Zhang
- Yukang Chen
Papers from Haitao Zheng's perspective
Ordered by how strongly the researchers Haitao Zheng trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- 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
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design (2018) — 6,472 citations
- Parameter-efficient fine-tuning of large-scale pre-trained language models (2023) — 1,192 citations
- Prototypical Representation Learning for Relation Extraction (2021) — 67 citations
- OpenPrompt: An Open-source Framework for Prompt-learning (2021) — 344 citations
- CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding (2021) — 133 citations
- Prompt-Learning for Fine-Grained Entity Typing (2021) — 176 citations
- Few-NERD: A Few-shot Named Entity Recognition Dataset (2021) — 303 citations