Tim Sainburg

Computer Vision and Pattern Recognition · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 66 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 50 citations into it
• Artificial Intelligence — 31 citations into it
• Generative Models — 30 citations into it
• Representation & Self-Supervised Learning — 30 citations into it
• Signal Processing — 14 citations into it

Whom Tim Sainburg trusts overall

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

1. Geoffrey E. Hinton
2. L. V. D. van der Maaten
3. Diederik P. Kingma
4. Yann LeCun
5. Yoshua Bengio
6. Alex Krizhevsky
7. Alec Radford
8. Radford M. Neal
9. Kaiming He
10. Alex Graves

Who trusts Tim Sainburg overall

1. Weixin Liang
2. Christopher Beckham
3. Yuhui Zhang

Papers from Tim Sainburg's perspective

Ordered by how strongly the researchers Tim Sainburg 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. 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. 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

• Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourage convex latent distributions (2018) — 56 citations
• Parametric UMAP Embeddings for Representation and Semisupervised Learning (2020) — 343 citations
• Finding, visualizing, and quantifying latent structure across diverse animal vocal repertoires (2020) — 304 citations