Kunle Olukotun
Artificial Intelligence · 2004–2019
A researcher in the Science corpus: 15 papers; trusts 66 researchers and is trusted by 33 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 96 citations into it; #8,790 of 35,345 by trust here, #8,525 by citations
- Computer Vision and Pattern Recognition — 88 citations into it; #7,925 of 21,575 by trust here, #9,187 by citations
- Optimization & Training — 68 citations into it; #1,488 of 2,601 by trust here, #1,046 by citations
- Computer Networks and Communications — 66 citations into it; #2,087 of 9,886 by trust here, #891 by citations
- Hardware and Architecture — 51 citations into it; #231 of 1,551 by trust here, #143 by citations
- Statistical and Nonlinear Physics — 21 citations into it
- Information Systems — 18 citations into it
- Computational Mechanics — 17 citations into it
Whom Kunle Olukotun trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- L. Eon Bottou
- Yann LeCun
- Alex Krizhevsky
- Kaiming He
- Joseph E. Gonzalez
- Geoffrey E. Hinton
- Yoshua Bengio
- Diederik P. Kingma
- Matthieu Latapy
- Ilya Sutskever
Who trusts Kunle Olukotun overall
- Junwhan Ahn
- Dan Alistarh
- Jakub Konecný
- Sungpack Hong
- Jimmy J. Lin
- Jichuan Chang
- Onur Mutlu
- Sebastian U. Stich
- T. Harris
- Sungjoo Yoo
Papers from Kunle Olukotun's perspective
Ordered by how strongly the researchers Kunle Olukotun trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark (2018) — 126 citations
- Taming the Wild: A Unified Analysis of Hogwild!-Style Algorithms (2015) — 210 citations
- The Future of Microprocessors (2005) — 712 citations
- High-Accuracy Low-Precision Training (2018) — 114 citations
- MLSys: The New Frontier of Machine Learning Systems (2019) — 25 citations
- DAWNBench : An End-to-End Deep Learning Benchmark and Competition (2017) — 353 citations
- EmptyHeaded: A Relational Engine for Graph Processing (2015) — 293 citations
- Green-Marl: a DSL for easy and efficient graph analysis (2012) — 328 citations
- Accelerating CUDA graph algorithms at maximum warp (2011) — 392 citations
- Efficient Parallel Graph Exploration on Multi-Core CPU and GPU (2011) — 294 citations
- STAMP: Stanford Transactional Applications for Multi-Processing (2008) — 1,007 citations
- An effective hybrid transactional memory system with strong isolation guarantees (2007) — 307 citations
- Map-Reduce for Machine Learning on Multicore (2006) — 1,288 citations
- Niagara: a 32-way multithreaded Sparc processor (2005) — 1,071 citations
- Transactional memory coherence and consistency (2004) — 771 citations