Mengjiao Yang
Artificial Intelligence · 2020–2023
A researcher in the Science corpus: 9 papers; trusts 366 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 288 citations into it
- Reinforcement Learning — 209 citations into it; #1,500 of 2,242 by trust here, #1,519 by citations
- Computer Vision and Pattern Recognition — 109 citations into it
- Management Science and Operations Research — 72 citations into it
- Natural Language Processing — 49 citations into it
- Computational Theory and Mathematics — 34 citations into it
- Large Language Models — 32 citations into it
- Model Architectures — 32 citations into it
Whom Mengjiao Yang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Richard S. Sutton
- Geoffrey E. Hinton
- Diederik P. Kingma
- Alex Krizhevsky
- Yoshua Bengio
- Kaiming He
- Yann LeCun
- Ofir Nachum
- Ronald J. Williams
- Sergey Levine
Who trusts Mengjiao Yang overall
- Ofir Nachum
- Zhuangdi Zhu
- Audrey Huang
- Cameron Voloshin
- Nan Jiang
- Scott Fujimoto
- Kaixiang Lin
- Justin Fu
- Hoang Minh Le
- Masatoshi Uehara
Papers from Mengjiao Yang's perspective
Ordered by how strongly the researchers Mengjiao Yang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- 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)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Combiner: Full Attention Transformer with Sparse Computation Cost (2021) — 99 citations
- Offline RL for Natural Language Generation with Implicit Language Q Learning (2022) — 154 citations
- Benchmarks for Deep Off-Policy Evaluation (2021) — 117 citations
- Provable Representation Learning for Imitation with Contrastive Fourier Features (2021) — 40 citations
- Learning Universal Policies via Text-Guided Video Generation (2023) — 599 citations
- Learning Interactive Real-World Simulators (2023) — 467 citations
- Offline Policy Selection under Uncertainty (2020) — 35 citations
- Off-Policy Evaluation via the Regularized Lagrangian (2020) — 122 citations
- Representation Matters: Offline Pretraining for Sequential Decision Making (2021) — 126 citations