Ruocheng Guo

Artificial Intelligence · 2018–2023

A researcher in the Science corpus: 6 papers; trusts 184 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 204 citations into it; #31,095 of 35,345 by trust here, #16,471 by citations
• Causal Inference & Discovery — 91 citations into it
• Statistics and Probability — 78 citations into it
• Computer Vision and Pattern Recognition — 61 citations into it; #19,292 of 21,575 by trust here, #20,756 by citations
• Safety, Robustness & Interpretability — 44 citations into it
• Probabilistic & Bayesian Methods — 27 citations into it
• Representation & Self-Supervised Learning — 25 citations into it
• Natural Language Processing — 20 citations into it

Whom Ruocheng Guo trusts overall

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

1. Judea Pearl
2. Donald B. Rubin
3. Geoffrey E. Hinton
4. Jan Peters
5. Aapo Hyvärinen
6. Peter Spirtes
7. Diederik P. Kingma
8. Bernhard Schölkopf
9. Robert Tibshirani
10. Alex Krizhevsky

Who trusts Ruocheng Guo overall

1. Bernhard Schölkopf
2. Dong Zhang
3. Amir-Hossein Karimi
4. Jaya Kalla

Papers from Ruocheng Guo's perspective

Ordered by how strongly the researchers Ruocheng Guo trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment (2023) — 575 citations
• Graph Few-shot Class-incremental Learning (2021) — 74 citations
• Causal Interpretability for Machine Learning - Problems, Methods and Evaluation (2020) — 266 citations
• Causal Learning in Question Quality Improvement (2019) — 7 citations
• A Survey of Learning Causality with Data (2018) — 184 citations
• A Survey of Learning Causality with Data: Problems and Methods (2018) — 335 citations