Jake Snell

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 5 papers; trusts 469 researchers and is trusted by 836 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 79 citations into it; #494 of 35,345 by trust here, #1,078 by citations
• Computer Vision and Pattern Recognition — 74 citations into it; #6,968 of 21,575 by trust here, #4,105 by citations
• Representation & Self-Supervised Learning — 45 citations into it; #82 of 3,145 by trust here, #131 by citations
• Generative Models — 22 citations into it; #1,291 of 1,956 by trust here, #1,415 by citations
• Evaluation & Benchmarks — 17 citations into it
• Probabilistic & Bayesian Methods — 17 citations into it
• Meta & Continual Learning — 11 citations into it
• Optimization & Training — 11 citations into it

Whom Jake Snell trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Alex Krizhevsky
4. Ruslan Salakhutdinov
5. Yoshua Bengio
6. Oriol Vinyals
7. Jimmy Ba
8. Yann LeCun
9. Kaiming He
10. Karen Simonyan

Who trusts Jake Snell overall

1. Han-Jia Ye
2. Spyros Gidaris
3. Da-Wei Zhou
4. De-chuan Zhan
5. Sergey Levine
6. Trevor Darrell
7. Mohammad Rostami
8. Chelsea Finn
9. Chi Zhang
10. Bernt Schiele

Papers from Jake Snell's perspective

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

Their papers

• Prototypical Networks for Few-shot Learning (2017) — 10,206 citations
• Meta-Learning for Semi-Supervised Few-Shot Classification (2018) — 1,444 citations
• Learning to generate images with perceptual similarity metrics (2015) — 206 citations
• Learning Latent Subspaces in Variational Autoencoders (2018) — 156 citations
• Bayesian Few-Shot Classification with One-vs-Each Pólya-Gamma Augmented Gaussian Processes (2020) — 68 citations