Huaxiu Yao

Artificial Intelligence · 2018–2024

A researcher in the Science corpus: 10 papers; trusts 175 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 167 citations into it; #30,165 of 35,345 by trust here, #4,923 by citations
• Computer Vision and Pattern Recognition — 96 citations into it; #18,512 of 21,575 by trust here, #12,992 by citations
• Representation & Self-Supervised Learning — 53 citations into it
• Natural Language Processing — 47 citations into it
• Evaluation & Benchmarks — 35 citations into it
• Meta & Continual Learning — 26 citations into it
• Building and Construction — 22 citations into it; #908 of 2,909 by trust here, #572 by citations
• Graph Learning — 20 citations into it

Whom Huaxiu Yao trusts overall

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

1. Chelsea Finn
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Alex Graves
6. Alex Krizhevsky
7. Oriol Vinyals
8. Yann LeCun
9. Sepp Hochreiter
10. Ilya Sutskever

Who trusts Huaxiu Yao overall

1. Han-Jia Ye
2. Saeed Rahmani
3. Fuxian Li
4. Jie Feng
5. Haitao Yuan
6. Li Xu
7. Yuanfu Lu
8. Lei Bai
9. Lin Wang
10. Senzhang Wang

Papers from Huaxiu Yao's perspective

Ordered by how strongly the researchers Huaxiu Yao 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Holistic Evaluation of Language Models (2023) — 1,863 citations
• Online Structured Meta-learning (2020) — 29 citations
• Hierarchically Structured Meta-learning (2019) — 223 citations
• Analyzing and Mitigating Object Hallucination in Large Vision-Language Models (2023) — 347 citations
• TrustLLM: Trustworthiness in Large Language Models (2024) — 353 citations
• Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback (2023) — 810 citations
• Graph Few-shot Learning via Knowledge Transfer (2019) — 194 citations
• Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction (2018) — 1,258 citations
• IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control (2018) — 771 citations
• Revisiting Spatial-Temporal Similarity: A Deep Learning Framework for Traffic Prediction (2018) — 833 citations