Nikko Ström
Artificial Intelligence · 1997–2019
A researcher in the Science corpus: 4 papers; trusts 53 researchers and is trusted by 147 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 28 citations into it; #1,906 of 35,345 by trust here, #9,465 by citations
- Signal Processing — 23 citations into it
- Speech & Audio — 14 citations into it
- Optimization & Training — 12 citations into it; #224 of 2,601 by trust here, #894 by citations
- Natural Language Processing — 10 citations into it; #1,610 of 5,688 by trust here, #3,897 by citations
- Efficiency & Compression — 5 citations into it; #231 of 1,264 by trust here, #1,140 by citations
Whom Nikko Ström trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Frank Torsten Bernd Seide
- Matthew D. Zeiler
- Yann LeCun
- Anthony J. Robinson
- Geoffrey E. Hinton
- John C. Duchi
- Adam Coates
- Dong Yu
- Yurii E. Nesterov
- Karel Veselý
Who trusts Nikko Ström overall
- Sebastian U. Stich
- Dan Alistarh
- Tim Dettmers
- Cong Xie
- Sara Hooker
- Trevor Gale
- Torsten Hoefler
- Tal Ben-Nun
- Adam Polyak
- Alham Fikri Aji
Papers from Nikko Ström's perspective
Ordered by how strongly the researchers Nikko Ström trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Scalable distributed DNN training using commodity GPU cloud computing (2015) — 591 citations
- Lessons from Building Acoustic Models with a Million Hours of Speech (2019) — 90 citations
- Data Augmentation for Robust Keyword Spotting under Playback Interference (2018) — 38 citations
- Sparse connection and pruning in large dynamic artificial neural networks (1997)