Ian N. Davidson

Artificial Intelligence · 2005–2021

A researcher in the Science corpus: 7 papers; trusts 109 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 53 citations into it; #10,360 of 35,345 by trust here, #17,412 by citations
• Computer Vision and Pattern Recognition — 51 citations into it; #9,255 of 21,575 by trust here, #16,367 by citations
• Representation & Self-Supervised Learning — 29 citations into it
• Model Architectures — 6 citations into it
• Optimization & Training — 6 citations into it

Whom Ian N. Davidson trusts overall

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

1. Kiri L. Wagstaff
2. Sugato Basu
3. L. Eon Bottou
4. Yoshua Bengio
5. Daniel Klein
6. Geoffrey E. Hinton
7. Claire Cardie
8. Kaiming He
9. Alex Krizhevsky
10. Yann LeCun

Who trusts Ian N. Davidson overall

1. Brian Kulis
2. Violaine Antoine
3. Yen-Chang Hsu
4. Syama Sundar Rangapuram
5. Russell Stewart
6. Sugato Basu
7. Benjamin Quost
8. Jagadeesh Jagarlamudi
9. Zhaoyang Lv
10. Inderjit S. Dhillon

Papers from Ian N. Davidson's perspective

Ordered by how strongly the researchers Ian N. Davidson 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• On constrained spectral clustering and its applications (2012) — 210 citations
• Constrained Clustering: Advances in Algorithms, Theory, and Applications (2008) — 544 citations
• Clustering with Constraints: Feasibility Issues and the k-Means Algorithm (2005) — 333 citations
• Multi-source domain adaptation and its application to early detection of fatigue (2011) — 57 citations
• A Self-Supervised Deep Learning Framework for Unsupervised Few-Shot Learning and Clustering (2021) — 11 citations
• Dense Transformer Networks for Brain Electron Microscopy Image Segmentation (2019) — 8 citations
• A Survey of Clustering with Instance Level Constraints 1 2 (2007)