Lieke Gelderloos
Computer Vision and Pattern Recognition · 2016–2017
A researcher in the Science corpus: 2 papers; trusts 121 researchers and is trusted by 23 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 31 citations into it; #4,622 of 21,575 by trust here, #8,205 by citations
- Artificial Intelligence — 28 citations into it; #13,277 of 35,345 by trust here, #23,077 by citations
- Multimodal Learning — 16 citations into it; #194 of 1,211 by trust here, #671 by citations
- Natural Language Processing — 15 citations into it
- Representation & Self-Supervised Learning — 10 citations into it
- Model Architectures — 8 citations into it
Whom Lieke Gelderloos trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- David F. Harwath
- Yoshua Bengio
- Geoffrey E. Hinton
- Ákos Kádár
- Diederik P. Kingma
- Kaiming He
- Alex Krizhevsky
- Ilya Sutskever
- James R. Glass
- Junyoung Chung
Who trusts Lieke Gelderloos overall
- Yonatan Belinkov
- Herman Kamper
- Grzegorz Chrupała
- Desmond Elliott
- David F. Harwath
- Afra Alishahi
- James R. Glass
- William N. Havard
- Ákos Kádár
- Karen Livescu
Papers from Lieke Gelderloos's perspective
Ordered by how strongly the researchers Lieke Gelderloos 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)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Learning Multiple Layers of Features from Tiny Images