Lin F. Yang

Artificial Intelligence · 2001–2023

A researcher in the Science corpus: 40 papers; trusts 1,433 researchers and is trusted by 62 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 307 citations into it; #7,605 of 35,345 by trust here, #4,489 by citations
• Computer Vision and Pattern Recognition — 303 citations into it; #4,544 of 21,575 by trust here, #3,323 by citations
• Reinforcement Learning — 141 citations into it
• Management Science and Operations Research — 113 citations into it; #3,204 of 11,946 by trust here, #2,241 by citations
• Environmental Engineering — 107 citations into it
• Biophysics — 58 citations into it; #678 of 1,196 by trust here, #482 by citations
• Model Architectures — 48 citations into it
• Generative Models — 46 citations into it; #487 of 1,956 by trust here, #748 by citations

Whom Lin F. Yang trusts overall

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

1. Geoffrey E. Hinton
2. Kaiming He
3. Alex Krizhevsky
4. Yann LeCun
5. Diederik P. Kingma
6. Yoshua Bengio
7. Richard S. Sutton
8. David G. Lowe
9. Sham M. Kakade
10. Ilya Sutskever

Who trusts Lin F. Yang overall

1. Qi Cai
2. Zhuoran Yang
3. Weitong Zhang
4. Yaqi Duan
5. Mengdi Wang
6. Zhaoran Wang
7. Alekh Agarwal
8. Chi Jin
9. Tengyang Xie
10. Masatoshi Uehara

Papers from Lin F. Yang's perspective

Ordered by how strongly the researchers Lin F. Yang 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Photographic Text-to-Image Synthesis with a Hierarchically-Nested Adversarial Network (2018) — 323 citations
• MDNet: A Semantically and Visually Interpretable Medical Image Diagnosis Network (2017) — 343 citations
• Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? (2019) — 215 citations
• Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network (2018) — 420 citations
• Combining Fully Convolutional and Recurrent Neural Networks for 3D Biomedical Image Segmentation (2016) — 320 citations
• Model-Based Reinforcement Learning with Value-Targeted Regression (2020) — 329 citations
• SemiContour: A Semi-supervised Learning Approach for Contour Detection (2016) — 50 citations
• Variance Reduction Methods for Sublinear Reinforcement Learning (2018) — 36 citations
• Preference-based Reinforcement Learning with Finite-Time Guarantees (2020) — 81 citations
• Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation (2017) — 560 citations
• Is Long Horizon Reinforcement Learning More Difficult Than Short Horizon Reinforcement Learning? (2020) — 53 citations
• On Reward-Free Reinforcement Learning with Linear Function Approximation (2020) — 119 citations
• Chinese companies’ awareness and perceptions of the Emissions Trading Scheme (ETS): Evidence from a national survey in China (2016) — 126 citations
• DNAshape: a method for the high-throughput prediction of DNA structural features on a genomic scale (2013) — 321 citations
• Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019 (2022) — 3,360 citations
• Global, regional, and national burden of low back pain, 1990–2020, its attributable risk factors, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021 (2023) — 1,077 citations
• Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021 (2023) — 2,755 citations
• Evaluation of conditioned Latin hypercube sampling for soil mapping based on a machine learning method (2020) — 60 citations
• Pathologist-level interpretable whole-slide cancer diagnosis with deep learning (2019) — 318 citations
• Sample-Optimal Parametric Q-Learning Using Linearly Additive Features (2019) — 347 citations