Austin Reiter

Computer Vision and Pattern Recognition · 2016–2021

A researcher in the Science corpus: 5 papers; trusts 746 researchers and is trusted by 43 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 145 citations into it; #4,195 of 21,575 by trust here, #5,013 by citations
• Artificial Intelligence — 103 citations into it; #22,310 of 35,345 by trust here, #13,353 by citations
• Representation & Self-Supervised Learning — 37 citations into it
• Model Architectures — 24 citations into it; #1,533 of 2,278 by trust here, #1,269 by citations
• Natural Language Processing — 16 citations into it
• Biomedical Engineering — 15 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Optimization & Training — 9 citations into it

Whom Austin Reiter trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Karen Simonyan
5. Yoshua Bengio
6. Ross B. Girshick
7. Diederik P. Kingma
8. Yann LeCun
9. Ilya Sutskever
10. Andrew Zisserman

Who trusts Austin Reiter overall

1. Yifan Zhang
2. Daochang Liu
3. Lei Shi
4. Jun Liu
5. Alexander Richard
6. Yazan Abu Farha
7. Juergen Gall
8. Jian Cheng
9. Jun Li
10. Pichao Wang

Papers from Austin Reiter's perspective

Ordered by how strongly the researchers Austin Reiter 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. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
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. Going deeper with convolutions

Their papers

• Temporal Convolutional Networks for Action Segmentation and Detection (2016) — 2,103 citations
• Segmental Spatiotemporal CNNs for Fine-Grained Action Segmentation (2016) — 301 citations
• Temporal Convolutional Networks: A Unified Approach to Action Segmentation (2016) — 1,028 citations
• Exploring Visual Engagement Signals for Representation Learning (2021) — 15 citations
• Interpretable 3D Human Action Analysis with Temporal Convolutional Networks (2017) — 661 citations