Daniel Crankshaw

Artificial Intelligence · 2014–2020

A researcher in the Science corpus: 6 papers; trusts 116 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 52 citations into it; #32,248 of 35,345 by trust here, #12,387 by citations
• Computer Vision and Pattern Recognition — 43 citations into it
• ML Systems & Hardware — 15 citations into it
• Model Architectures — 15 citations into it
• Reinforcement Learning — 12 citations into it
• Management Science and Operations Research — 9 citations into it

Whom Daniel Crankshaw trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Karen Simonyan
5. Kaiming He
6. Ilya Sutskever
7. Christian Szegedy
8. Yann LeCun
9. Andrew Zisserman
10. Radford M. Neal

Who trusts Daniel Crankshaw overall

1. Arpan Gujarati
2. Christopher Olston
3. Peifeng Yu
4. Xin Wang
5. Chengliang Zhang
6. Jiarui Fang
7. Firas Abuzaid
8. Joseph M Hellerstein
9. Sanjay Krishnan
10. Hong Zhang

Papers from Daniel Crankshaw's perspective

Ordered by how strongly the researchers Daniel Crankshaw trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Clipper: A Low-Latency Online Prediction Serving System (2016) — 841 citations
• IDK Cascades: Fast Deep Learning by Learning not to Overthink (2017) — 136 citations
• Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning (2017) — 26 citations
• InferLine: latency-aware provisioning and scaling for prediction serving pipelines (2020) — 173 citations
• The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox (2014) — 126 citations
• GraphX: Graph Processing in a Distributed Dataflow Framework (2014) — 1,216 citations