Benjamin Klein

Computer Vision and Pattern Recognition · 2014–2015

A researcher in the Science corpus: 6 papers; trusts 815 researchers and is trusted by 465 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 149 citations into it; #1,967 of 21,575 by trust here, #3,692 by citations
• Artificial Intelligence — 82 citations into it; #5,980 of 35,345 by trust here, #8,706 by citations
• Natural Language Processing — 62 citations into it; #4,619 of 5,688 by trust here, #5,412 by citations
• Multimodal Learning — 35 citations into it; #339 of 1,211 by trust here, #671 by citations
• Representation & Self-Supervised Learning — 23 citations into it; #1,058 of 3,145 by trust here, #1,493 by citations
• Model Architectures — 20 citations into it
• Evaluation & Benchmarks — 15 citations into it
• Media Technology — 15 citations into it; #2,292 of 3,621 by trust here, #1,859 by citations

Whom Benjamin Klein trusts overall

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

1. Geoffrey E. Hinton
2. Karen Simonyan
3. Yoshua Bengio
4. Yann LeCun
5. Florent Perronnin
6. Alex Krizhevsky
7. Radford M. Neal
8. Ilya Sutskever
9. Tomas Mikolov
10. Andrew Zisserman

Who trusts Benjamin Klein overall

1. Yan Huang
2. Antoine Miech
3. Liwei Wang
4. Bryan A. Plummer
5. Zhedong Zheng
6. Chaopeng Shen
7. Svetlana Lazebnik
8. Yuxin Peng
9. Jianfeng Dong
10. Lior Wolf

Papers from Benjamin Klein's perspective

Ordered by how strongly the researchers Benjamin Klein 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

• Associating neural word embeddings with deep image representations using Fisher Vectors (2015) — 302 citations
• Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A (2015) — 720 citations
• RNN Fisher Vectors for Action Recognition and Image Annotation (2015) — 172 citations
• Fisher Vectors Derived from Hybrid Gaussian-Laplacian Mixture Models for Image Annotation (2014) — 103 citations
• A Dynamic Convolutional Layer for short rangeweather prediction (2015) — 189 citations
• In Defense of Word Embedding for Generic Text Representation (2015) — 41 citations