Meital Zilberstein

Natural Language Processing · 2018

A researcher in the Science corpus: 1 paper; trusts 31 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Natural Language Processing — 41 citations into it; #2,011 of 5,688 by trust here, #2,634 by citations
• Artificial Intelligence — 31 citations into it; #10,506 of 35,345 by trust here, #12,244 by citations
• Information Systems — 24 citations into it; #1,716 of 8,771 by trust here, #1,933 by citations
• Software — 14 citations into it
• Code Generation & Program Synthesis — 11 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it
• Representation & Self-Supervised Learning — 10 citations into it; #1,242 of 3,145 by trust here, #1,597 by citations
• Model Architectures — 6 citations into it

Whom Meital Zilberstein trusts overall

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

1. Miltiadis Allamanis
2. Yoshua Bengio
3. Diederik P. Kingma
4. Omer Levy
5. Xavier Glorot
6. Geoffrey E. Hinton
7. Dzmitry Bahdanau
8. Tomas Mikolov
9. Ilya Sutskever
10. Ronan Collobert

Who trusts Meital Zilberstein overall

1. Uri Alon
2. Aditya Kanade
3. Miltiadis Allamanis
4. Shaked Brody
5. Eran Yahav
6. Goutham Ramakrishnan
7. Lutz Büch
8. Seohyun Kim
9. Tal Ben-Nun
10. Noam Yefet

Papers from Meital Zilberstein's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. ImageNet: A large-scale hierarchical image database
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• code2vec: learning distributed representations of code (2018) — 1,357 citations