Daniel Ritchie

Computer Vision and Pattern Recognition · 2012–2018

A researcher in the Science corpus: 6 papers; trusts 274 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 82 citations into it; #9,979 of 21,575 by trust here, #8,021 by citations
• Artificial Intelligence — 46 citations into it
• Probabilistic & Bayesian Methods — 27 citations into it
• Generative Models — 26 citations into it
• Computational Mechanics — 23 citations into it; #4,090 of 10,001 by trust here, #2,112 by citations
• Computer Graphics and Computer-Aided Design — 22 citations into it; #659 of 1,584 by trust here, #464 by citations
• 3D Vision & Pose — 17 citations into it; #585 of 689 by trust here, #529 by citations
• Model Architectures — 11 citations into it

Whom Daniel Ritchie trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Alex Krizhevsky
5. Kaiming He
6. Alex Graves
7. Andriy Mnih
8. David Wingate
9. J. Urgen Schmidhuber
10. Yann LeCun

Who trusts Daniel Ritchie overall

1. Willie Neiswanger
2. P. Hanrahan
3. Angel X. Chang
4. Manolis Savva
5. Shakir Mohamed
6. Marco F. Cusumano-Towner
7. Niloy J. Mitra
8. Andrew Cropper
9. Angela Dai
10. Yura N. Perov

Papers from Daniel Ritchie's perspective

Ordered by how strongly the researchers Daniel Ritchie trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Example-based synthesis of 3D object arrangements (2012) — 413 citations
• Deep Amortized Inference for Probabilistic Programs (2016) — 87 citations
• ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans (2017) — 332 citations
• Improving Shape Deformation in Unsupervised Image-to-Image Translation (2018) — 78 citations
• C3: Lightweight Incrementalized MCMC for Probabilistic Programs using Continuations and Callsite Caching (2015) — 33 citations
• Learning to Infer Graphics Programs from Hand-Drawn Images (2017) — 252 citations