Karl Ridgeway

Computer Vision and Pattern Recognition · 2015–2018

A researcher in the Science corpus: 4 papers; trusts 706 researchers and is trusted by 44 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 81 citations into it; #5,894 of 21,575 by trust here, #9,305 by citations
• Representation & Self-Supervised Learning — 55 citations into it; #557 of 3,145 by trust here, #1,507 by citations
• Artificial Intelligence — 49 citations into it; #9,469 of 35,345 by trust here, #25,809 by citations
• Signal Processing — 17 citations into it
• Generative Models — 14 citations into it
• Evaluation & Benchmarks — 10 citations into it
• Media Technology — 8 citations into it
• Model Architectures — 7 citations into it

Whom Karl Ridgeway trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yann LeCun
4. Aapo Hyvärinen
5. Alex Krizhevsky
6. Yoshua Bengio
7. J. Urgen Schmidhuber
8. Scott E. Reed
9. Sumit Chopra
10. Ilya Sutskever

Who trusts Karl Ridgeway overall

1. Francesco Locatello
2. Kien Do
3. Bernhard Schölkopf
4. Raphael Suter
5. Han-Jia Ye
6. Stefan Bauer
7. Aviv Gabbay
8. Drew A. Hudson
9. Truyen Tran
10. Abhishek Kumar

Papers from Karl Ridgeway's perspective

Ordered by how strongly the researchers Karl Ridgeway 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Learning Deep Disentangled Embeddings with the F-Statistic Loss (2018) — 238 citations
• Learning to generate images with perceptual similarity metrics (2015) — 206 citations
• A Survey of Inductive Biases for Factorial Representation-Learning (2016) — 81 citations
• Adapted Deep Embeddings: A Synthesis of Methods for k-Shot Inductive Transfer Learning (2018) — 90 citations