Arun Kumar
Artificial Intelligence · with Li · 2012–2019
A researcher in the Science corpus: 9 papers; trusts 33 researchers and is trusted by 12 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 37 citations into it; #15,388 of 35,345 by trust here, #9,170 by citations
- Optimization & Training — 27 citations into it; #1,881 of 2,601 by trust here, #1,225 by citations
- Information Systems — 21 citations into it
- Statistics and Probability — 14 citations into it
- Computer Vision and Pattern Recognition — 12 citations into it
- Computer Networks and Communications — 11 citations into it; #5,038 of 9,886 by trust here, #2,761 by citations
- Evaluation & Benchmarks — 10 citations into it
- Statistical Learning Theory — 8 citations into it
Whom Arun Kumar trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David Ruppert
- L. Eon Bottou
- Diederik P. Kingma
- Dimitri P. Bertsekas
- Robert Tibshirani
- James Franklin
- Martin A. Zinkevich
- Shai Shalev-Shwartz
- Isabelle M Guyon
- Yann LeCun
Who trusts Arun Kumar overall
- Sanjay Krishnan
- H. Brendan McMahan
- Peter Bailis
- Evan R. Sparks
- M. Franklin
- Vitaly Feldman
- Ce Zhang
- Side Li
- Henry R. Ehrenberg
- Om Thakkar
Papers from Arun Kumar's perspective
Ordered by how strongly the researchers Arun Kumar trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- 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)
- Learning Multiple Layers of Features from Tiny Images
- Et al
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Towards a unified architecture for in-RDBMS analytics (2012) — 211 citations
- Materialization optimizations for feature selection workloads (2014) — 147 citations
- Bolt-on Differential Privacy for Scalable Stochastic Gradient Descent-based Analytics (2016) — 239 citations
- Towards Linear Algebra over Normalized Data (2016) — 99 citations
- SysML: The New Frontier of Machine Learning Systems (2019) — 23 citations
- MLSys: The New Frontier of Machine Learning Systems (2019) — 25 citations
- Enabling and Optimizing Non-linear Feature Interactions in Factorized Linear Algebra (2019) — 38 citations
- Are Key-Foreign Key Joins Safe to Avoid when Learning High-Capacity Classifiers? (2017) — 27 citations
- The MADlib Analytics Library or MAD Skills, the SQL (2012) — 449 citations