Brandon Yang
Artificial Intelligence · 2017–2019
A researcher in the Science corpus: 9 papers; trusts 329 researchers and is trusted by 11 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 145 citations into it; #14,983 of 35,345 by trust here, #12,083 by citations
- Computer Vision and Pattern Recognition — 135 citations into it; #9,124 of 21,575 by trust here, #2,768 by citations
- Reinforcement Learning — 68 citations into it
- Model Architectures — 34 citations into it
- Optimization & Training — 30 citations into it
- Evaluation & Benchmarks — 27 citations into it
- Management Science and Operations Research — 23 citations into it
- Efficiency & Compression — 14 citations into it; #643 of 1,264 by trust here, #827 by citations
Whom Brandon Yang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- Kaiming He
- Yoshua Bengio
- Diederik P. Kingma
- Yann LeCun
- Ilya Sutskever
- Ross B. Girshick
- Quoc V. Le
- Christian Szegedy
Who trusts Brandon Yang overall
- Yinpeng Chen
- Xiyang Dai
- Mengchen Liu
- Zhi Tian
- Ningning Ma
- Chao Li
- X. Zhang
- Vaishaal Shankar
- Huiyu Wang
- Lizhe Liu
Papers from Brandon Yang's perspective
Ordered by how strongly the researchers Brandon Yang 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)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- 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
- CondConv: Conditionally Parameterized Convolutions for Efficient Inference (2019) — 870 citations
- CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning (2017) — 3,431 citations
- Using Videos to Evaluate Image Model Robustness (2019) — 44 citations
- StarNet: Targeted Computation for Object Detection in Point Clouds (2019) — 130 citations
- Surprising Negative Results for Generative Adversarial Tree Search (2018) — 14 citations
- Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists (2018) — 1,270 citations
- MURA Dataset: Towards Radiologist-Level Abnormality Detection in Musculoskeletal Radiographs (2017) — 89 citations
- Sample-Efficient Deep RL with Generative Adversarial Tree Search (2018) — 17 citations
- MURA: Large Dataset for Abnormality Detection in Musculoskeletal Radiographs. (2017) — 410 citations