Chris Kedzie

Natural Language Processing · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 80 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Natural Language Processing — 34 citations into it; #2,238 of 5,688 by trust here, #2,955 by citations
• Artificial Intelligence — 33 citations into it; #12,853 of 35,345 by trust here, #17,412 by citations
• Computer Vision and Pattern Recognition — 7 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Chris Kedzie trusts overall

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

1. Kyunghyun Cho
2. Yoshua Bengio
3. Dzmitry Bahdanau
4. Diederik P. Kingma
5. Yoon Kim
6. Geoffrey E. Hinton
7. Chin-Yew Lin
8. Ilya Sutskever
9. Jimmy Ba
10. Xavier Glorot

Who trusts Chris Kedzie overall

1. Yixin Liu
2. Wen Xiao
3. Pengfei Liu
4. Jesse Vig
5. Wojciech Kryscinski
6. Faisal Ladhak
7. Byeongchang Kim
8. Zi-Yi Dou
9. Karan Goel
10. Shreyan Bakshi

Papers from Chris Kedzie's perspective

Ordered by how strongly the researchers Chris Kedzie 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. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Content Selection in Deep Learning Models of Summarization (2018) — 156 citations
• A Good Sample is Hard to Find: Noise Injection Sampling and Self-Training for Neural Language Generation Models (2019) — 37 citations