Evan R. Sparks

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 7 papers; trusts 58 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 47 citations into it; #20,245 of 35,345 by trust here, #7,346 by citations
• Computer Vision and Pattern Recognition — 34 citations into it
• Optimization & Training — 25 citations into it; #1,608 of 2,601 by trust here, #1,866 by citations
• Computer Networks and Communications — 22 citations into it
• Evaluation & Benchmarks — 19 citations into it
• ML Systems & Hardware — 13 citations into it
• Model Architectures — 8 citations into it
• Numerical Methods & Scientific Computing — 5 citations into it

Whom Evan R. Sparks trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. James Bergstra
5. David G. Lowe
6. M. Powell
7. Jia Deng
8. Ali Rahimi
9. Yann LeCun
10. John C. Duchi

Who trusts Evan R. Sparks overall

1. Kevin G. Jamieson
2. Sanjay Krishnan
3. Lisha Li
4. Gang Luo
5. Marc-André Zöller
6. Ameet Talwalkar
7. Tong Yu
8. Li Yang
9. D. Narayanan
10. Aaron Harlap

Papers from Evan R. Sparks's perspective

Ordered by how strongly the researchers Evan R. Sparks 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Going deeper with convolutions
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• MLlib: Machine Learning in Apache Spark (2015) — 1,861 citations
• Automating model search for large scale machine learning (2015) — 166 citations
• SysML: The New Frontier of Machine Learning Systems (2019) — 23 citations
• MLSys: The New Frontier of Machine Learning Systems (2019) — 25 citations
• Paleo: A Performance Model for Deep Neural Networks (2016) — 252 citations
• KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics (2016) — 161 citations
• TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries (2015) — 26 citations