William Lotter

Computer Vision and Pattern Recognition · 2015–2020

A researcher in the Science corpus: 3 papers; trusts 345 researchers and is trusted by 318 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 55 citations into it; #2,277 of 21,575 by trust here, #5,806 by citations
• Artificial Intelligence — 38 citations into it
• Representation & Self-Supervised Learning — 22 citations into it
• Generative Models — 15 citations into it; #393 of 1,956 by trust here, #636 by citations
• Model Architectures — 14 citations into it
• Cognitive Neuroscience — 8 citations into it
• Media Technology — 6 citations into it
• Optimization & Training — 5 citations into it

Whom William Lotter trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Nitish Srivastava
4. Alex Graves
5. Ilya Sutskever
6. Diederik P. Kingma
7. Alex Krizhevsky
8. Yann LeCun
9. Sepp Hochreiter
10. Marc'Aurelio Ranzato

Who trusts William Lotter overall

1. Chelsea Finn
2. Adam Henry Marblestone
3. Sergey Levine
4. Garrett Wilson
5. Ting-Chun Wang
6. Christoph Feichtenhofer
7. Sergiu Oprea
8. Xiaodan Liang
9. Oncel Tuzel
10. Sjoerd van Steenkiste

Papers from William Lotter's perspective

Ordered by how strongly the researchers William Lotter 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. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning (2016) — 1,007 citations
• Unsupervised Learning of Visual Structure using Predictive Generative Networks (2015) — 133 citations
• Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms (2020) — 345 citations