Panna Felsen
Computer Vision and Pattern Recognition · 2014–2018
A researcher in the Science corpus: 5 papers; trusts 825 researchers and is trusted by 360 researchers here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 144 citations into it; #2,140 of 21,575 by trust here, #5,053 by citations
- Artificial Intelligence — 47 citations into it; #30,206 of 35,345 by trust here, #27,551 by citations
- Control and Systems Engineering — 16 citations into it; #2,058 of 8,257 by trust here, #1,475 by citations
- Computational Mechanics — 14 citations into it
- Aerospace Engineering — 12 citations into it
- Generative Models — 12 citations into it
- 3D Vision & Pose — 10 citations into it
- Model Architectures — 10 citations into it
Whom Panna Felsen trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Alex Krizhevsky
- Yann LeCun
- Ross B. Girshick
- Kaiming He
- Ilya Sutskever
- Jitendra Malik
- Yoshua Bengio
- Diederik P. Kingma
- Karen Simonyan
Who trusts Panna Felsen overall
- Chen Sun
- Antonino Furnari
- Tsung-Yu Lin
- Ye Yuan
- Fanyi Xiao
- Wenguan Wang
- Michael J. Black
- Suyog Dutt Jain
- Muhammed Kocabas
- Wei Mao
Papers from Panna Felsen's perspective
Ordered by how strongly the researchers Panna Felsen trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet Large Scale Visual Recognition Challenge
- Going deeper with convolutions
Their papers
- Recurrent Network Models for Human Dynamics (2015) — 1,035 citations
- Learning 3D Human Dynamics From Video (2018) — 610 citations
- Learning to segment moving objects in videos (2014) — 214 citations
- What will Happen Next? Forecasting Player Moves in Sports Videos (2017) — 81 citations
- Spatio-Temporal Moving Object Proposals (2014) — 11 citations