Roy R. Lederman

Representation & Self-Supervised Learning · 2015–2018

A researcher in the Science corpus: 2 papers; trusts 38 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Representation & Self-Supervised Learning — 19 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it
• Artificial Intelligence — 14 citations into it; #34,647 of 35,345 by trust here, #30,347 by citations
• Media Technology — 7 citations into it

Whom Roy R. Lederman trusts overall

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

1. Nitish Srivastava
2. Gregory R. Koch
3. Yann LeCun
4. Ronald R. Coifman
5. Geoffrey E. Hinton
6. Yoshua Bengio
7. Raia Hadsell
8. Alex Krizhevsky
9. Graham W. Taylor
10. Ruslan Salakhutdinov

Who trusts Roy R. Lederman overall

1. Uri Shaham

Papers from Roy R. Lederman's perspective

Ordered by how strongly the researchers Roy R. Lederman 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Learning the geometry of common latent variables using alternating-diffusion (2015) — 113 citations
• Learning by coincidence: Siamese networks and common variable learning (2018) — 38 citations