Larry Yang
Artificial Intelligence · 2017–2019
A researcher in the Science corpus: 3 papers; trusts 263 researchers and is trusted by 10 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 91 citations into it; #13,087 of 35,345 by trust here, #12,387 by citations
- Reinforcement Learning — 63 citations into it; #1,633 of 2,242 by trust here, #1,519 by citations
- Computer Vision and Pattern Recognition — 51 citations into it
- Control and Systems Engineering — 43 citations into it; #2,485 of 8,257 by trust here, #1,409 by citations
- Robotics & Control — 17 citations into it; #351 of 609 by trust here, #384 by citations
- Biomedical Engineering — 11 citations into it
- Generative Models — 10 citations into it
- Optimization & Training — 9 citations into it
Whom Larry Yang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Sergey Levine
- Diederik P. Kingma
- Geoffrey E. Hinton
- Alex Krizhevsky
- Pieter Abbeel
- Yoshua Bengio
- Yann LeCun
- John Schulman
- Karen Simonyan
- Chelsea Finn
Who trusts Larry Yang overall
- Chelsea Finn
- Laura M. Smith
- Sergey Levine
- Corey Lynch
- Brian Ichter
- Suraj Nair
- Henry Zhu
- Lars Berscheid
- Aravind Rajeswaran
- Avi Singh
Papers from Larry Yang's perspective
Ordered by how strongly the researchers Larry 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)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- 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)
- ImageNet Large Scale Visual Recognition Challenge
Their papers
- End-to-End Robotic Reinforcement Learning without Reward Engineering (2019) — 289 citations
- Variational Inverse Control with Events: A General Framework for Data-Driven Reward Definition (2018) — 133 citations
- GPLAC: Generalizing Vision-Based Robotic Skills Using Weakly Labeled Images (2017) — 23 citations