Biwei Huang

Artificial Intelligence · 2015–2021

A researcher in the Science corpus: 12 papers; trusts 127 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 158 citations into it; #20,962 of 35,345 by trust here, #13,962 by citations
• Causal Inference & Discovery — 72 citations into it; #203 of 364 by trust here, #124 by citations
• Probabilistic & Bayesian Methods — 51 citations into it
• Reinforcement Learning — 45 citations into it
• Signal Processing — 31 citations into it
• Computer Vision and Pattern Recognition — 30 citations into it
• Representation & Self-Supervised Learning — 28 citations into it
• Statistics and Probability — 26 citations into it

Whom Biwei Huang trusts overall

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

1. Kun Zhang
2. Bernhard Schölkopf
3. Aapo Hyvärinen
4. Tom L. Burr
5. Jan Peters
6. Geoffrey E. Hinton
7. David Maxwell Chickering
8. Patrik O. Hoyer
9. Arthur Gretton
10. Diederik P. Kingma

Who trusts Biwei Huang overall

1. Kun Zhang
2. Bernhard Schölkopf
3. Clark Glymour
4. Jiji Zhang
5. Mingming Gong
6. Matthew James Vowels
7. Ignavier Ng
8. Sébastien Lachapelle
9. Francesco Locatello
10. Ruben Sanchez-Romero

Papers from Biwei Huang's perspective

Ordered by how strongly the researchers Biwei Huang 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Causal Discovery from Heterogeneous/Nonstationary Data (2019) — 316 citations
• Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation (2020) — 77 citations
• Behind Distribution Shift: Mining Driving Forces of Changes and Causal Arrows (2017) — 38 citations
• AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning (2021) — 77 citations
• Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models (2019) — 75 citations
• Generalized Score Functions for Causal Discovery (2018) — 204 citations
• Causal Generative Domain Adaptation Networks (2018) — 22 citations
• Discovery and Visualization of Nonstationary Causal Models (2015) — 15 citations
• Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering (2019) — 28 citations
• Multi-domain Causal Structure Learning in Linear Systems (2018) — 65 citations
• Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination (2017) — 160 citations
• Identification of Time-Dependent Causal Model: A Gaussian Process Treatment (2015) — 47 citations