Aapo Kyrola
Artificial Intelligence · 2010–2017
A researcher in the Science corpus: 9 papers; trusts 91 researchers and is trusted by 1,121 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 48 citations into it; #2,164 of 35,345 by trust here, #1,223 by citations
- Computer Vision and Pattern Recognition — 46 citations into it; #1,294 of 21,575 by trust here, #1,002 by citations
- Computer Networks and Communications — 45 citations into it; #9,195 of 9,886 by trust here, #3,426 by citations
- Information Systems — 29 citations into it; #8,689 of 8,771 by trust here, #1,964 by citations
- Optimization & Training — 26 citations into it; #465 of 2,601 by trust here, #131 by citations
- ML Systems & Hardware — 18 citations into it; #482 of 575 by trust here, #177 by citations
- Hardware and Architecture — 16 citations into it; #1,528 of 1,551 by trust here, #318 by citations
- Statistical and Nonlinear Physics — 13 citations into it
Whom Aapo Kyrola trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Robert Tibshirani
- Muthu Dayalan
- Yucheng Low
- Joseph E. Gonzalez
- Charles E. Leiserson
- Xiaojin Zhu
- Dimitri P. Bertsekas
- Andrew Y. Ng
- Yunhong Zhou
- Shai Shalev-Shwartz
Who trusts Aapo Kyrola overall
- Peter Richtárik
- Martin Takác
- Eric P. Xing
- Qirong Ho
- Mu Li
- Ziqi Liu
- Alex Smola
- Prem Gopalan
- Chaoxia Wu
- Bin Shao
Papers from Aapo Kyrola's perspective
Ordered by how strongly the researchers Aapo Kyrola 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
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour (2017) — 4,148 citations
- Parallel Coordinate Descent for L1-Regularized Loss Minimization (2011) — 311 citations
- Distributed GraphLab: A Framework for Machine Learning in the Cloud (2012) — 1,087 citations
- GraphLab: A New Framework For Parallel Machine Learning (2010) — 927 citations
- Distributed GraphLab: A Framework for Machine Learning in the Cloud (2012) — 687 citations
- Kineograph: taking the pulse of a fast-changing and connected world (2012) — 305 citations
- GraphChi: Large-Scale Graph Computation on Just a PC (2012) — 1,210 citations
- GraphLab: A Distributed Framework for Machine Learning in the Cloud (2011) — 24 citations
- GraphLab: A New Parallel Framework for Machine Learning (2010) — 471 citations