G. R. Ganger
Artificial Intelligence · 2005–2019
A researcher in the Science corpus: 17 papers; trusts 271 researchers and is trusted by 17 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 116 citations into it; #27,003 of 35,345 by trust here, #8,210 by citations
- Computer Vision and Pattern Recognition — 92 citations into it; #16,546 of 21,575 by trust here, #5,620 by citations
- Optimization & Training — 74 citations into it; #1,558 of 2,601 by trust here, #606 by citations
- Computer Networks and Communications — 65 citations into it; #8,890 of 9,886 by trust here, #1,297 by citations
- ML Systems & Hardware — 36 citations into it; #181 of 575 by trust here, #111 by citations
- Hardware and Architecture — 31 citations into it
- Information Systems — 30 citations into it; #7,683 of 8,771 by trust here, #2,361 by citations
- Natural Language Processing — 25 citations into it
Whom G. R. Ganger trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Alex Krizhevsky
- Yoshua Bengio
- Geoffrey E. Hinton
- Yann LeCun
- Christian Szegedy
- Diederik P. Kingma
- Ilya Sutskever
- Karen Simonyan
- Henggang Cui
- Kaiming He
Who trusts G. R. Ganger overall
- Kevin Hsieh
- Aaron Harlap
- Sayed Hadi Hashemi
- Henggang Cui
- Amar Phanishayee
- Hyeontaek Lim
- D. Narayanan
- Cong Xie
- Wei Dai
- Sangeetha Abdu Jyothi
Papers from G. R. Ganger's perspective
Ordered by how strongly the researchers G. R. Ganger 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- ImageNet Large Scale Visual Recognition Challenge
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- PipeDream: generalized pipeline parallelism for DNN training (2019) — 1,228 citations
- PipeDream: Fast and Efficient Pipeline Parallel DNN Training (2018) — 317 citations
- SysML: The New Frontier of Machine Learning Systems (2019) — 23 citations
- MLSys: The New Frontier of Machine Learning Systems (2019) — 25 citations
- PipeDream: Pipeline Parallelism for DNN Training (2018)
- Gaia: Geo-Distributed Machine Learning Approaching LAN Speeds (2017) — 450 citations
- GeePS: scalable deep learning on distributed GPUs with a GPU-specialized parameter server (2016) — 337 citations
- Addressing the straggler problem for iterative convergent parallel ML (2016) — 148 citations
- Scalable deep learning on distributed GPUs with a GPU-specialized parameter server (2015)
- Exploiting iterative-ness for parallel ML computations (2014) — 40 citations
- Exploiting Bounded Staleness to Speed Up Big Data Analytics (2014) — 171 citations
- More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server (2013) — 813 citations
- Solving the Straggler Problem with Bounded Staleness (2013) — 149 citations
- Heterogeneity and dynamicity of clouds at scale: Google trace analysis (2012) — 1,197 citations
- Safe and effective fine-grained TCP retransmissions for datacenter communication (2009) — 499 citations
- Measurement and Analysis of TCP Throughput Collapse in Cluster-based Storage Systems (2008) — 295 citations
- Fault-scalable Byzantine fault-tolerant services (2005) — 466 citations