Tim Kraska

Artificial Intelligence · 2008–2020

A researcher in the Science corpus: 23 papers; trusts 128 researchers and is trusted by 30 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 188 citations into it; #6,163 of 35,345 by trust here, #5,495 by citations
• Computer Networks and Communications — 86 citations into it; #7,421 of 9,886 by trust here, #3,862 by citations
• Information Systems — 78 citations into it; #8,720 of 8,771 by trust here, #4,188 by citations
• Computer Vision and Pattern Recognition — 71 citations into it
• Management Science and Operations Research — 61 citations into it; #981 of 11,946 by trust here, #857 by citations
• Optimization & Training — 44 citations into it
• Signal Processing — 42 citations into it; #1,596 of 7,572 by trust here, #1,669 by citations
• Evaluation & Benchmarks — 30 citations into it

Whom Tim Kraska trusts overall

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

1. Alex Graves
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Yoshua Bengio
5. Jerome H. Friedman
6. Peter Christen
7. Alex Krizhevsky
8. Diederik P. Kingma
9. Chih-Jen Lin
10. Yann LeCun

Who trusts Tim Kraska overall

1. Sanjay Krishnan
2. Adam Marcus
3. M. Franklin
4. Gang Luo
5. Jiannan Wang
6. Evan R. Sparks
7. Ihab F. Ilyas
8. Eugene Wu
9. Kevin G. Jamieson
10. Ameet Talwalkar

Papers from Tim Kraska's perspective

Ordered by how strongly the researchers Tim Kraska 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

• Automating model search for large scale machine learning (2015) — 166 citations
• A sample-and-clean framework for fast and accurate query processing on dirty data (2014) — 127 citations
• The Case for Learned Index Structures (2017) — 1,261 citations
• CrowdER: Crowdsourcing Entity Resolution (2012) — 611 citations
• Crowdsourced enumeration queries (2013) — 120 citations
• CrowdDB: answering queries with crowdsourcing (2011) — 726 citations
• Stale View Cleaning: Getting Fresh Answers from Stale Materialized Views (2015) — 28 citations
• MISIM: An End-to-End Neural Code Similarity System (2020) — 24 citations
• Bao: Making Learned Query Optimization Practical (2020) — 321 citations
• Neo: A Learned Query Optimizer (2019) — 518 citations
• SysML: The New Frontier of Machine Learning Systems (2019) — 23 citations
• MLSys: The New Frontier of Machine Learning Systems (2019) — 25 citations
• Democratizing Data Science through Interactive Curation of ML Pipelines (2019) — 107 citations
• Superneurons: dynamic GPU memory management for training deep neural networks (2018) — 294 citations
• A Data Quality Metric (DQM): How to Estimate the Number of Undetected Errors in Data Sets (2016) — 21 citations
• PrivateClean: Data Cleaning and Differential Privacy (2016) — 38 citations
• Controlling False Discoveries During Interactive Data Exploration (2016) — 75 citations
• TuPAQ: An Efficient Planner for Large-scale Predictive Analytic Queries (2015) — 26 citations
• SampleClean: Fast and Reliable Analytics on Dirty Data (2015) — 61 citations
• Tupleware: Distributed Machine Learning on Small Clusters (2014) — 17 citations