Noah Weber

Artificial Intelligence · 2018

A researcher in the Science corpus: 1 paper; trusts 5 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 12 citations into it; #26,429 of 35,345 by trust here, #29,412 by citations
• Natural Language Processing — 11 citations into it; #4,342 of 5,688 by trust here, #5,092 by citations
• Model Architectures — 7 citations into it

Whom Noah Weber trusts overall

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

1. Sepp Hochreiter
2. Brenden M. Lake
3. P. Rodríguez
4. Jeffrey L. Elman
5. J. Urgen Schmidhuber
6. Marco Baroni
7. Janet Wiles
8. Yonatan Belinkov
9. Geoffrey E. Hinton
10. Leslie G. Valiant

Who trusts Noah Weber overall

1. R. Thomas McCoy
2. Dieuwke Hupkes
3. Yotam Gil
4. Junghyun Min
5. Tal Linzen
6. Yoav Chai
7. Anand Singh

Papers from Noah Weber's perspective

Ordered by how strongly the researchers Noah Weber trusts endorse each paper. Same corpus, a different order for every researcher.

1. Long Short-Term Memory
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Gradient-based learning applied to document recognition
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• The Fine Line between Linguistic Generalization and Failure in Seq2Seq-Attention Models (2018) — 30 citations