David Burshtein

Artificial Intelligence · 2001–2017

A researcher in the Science corpus: 3 papers; trusts 8 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 19 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it
• Natural Language Processing — 7 citations into it
• Electrical and Electronic Engineering — 6 citations into it
• Computer Networks and Communications — 5 citations into it; #3,035 of 9,886 by trust here, #5,518 by citations

Whom David Burshtein trusts overall

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

1. T. Richardson
2. Geoffrey E. Hinton
3. Alex Graves
4. Diederik P. Kingma

Who trusts David Burshtein overall

1. Hao Ye
2. Shmulik Markovich Golan
3. Bracha Laufer-Goldshtein
4. Mehrez Souden
5. Sharon Gannot
6. Neev Samuel
7. Tim O'Shea

Papers from David Burshtein's perspective

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

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

Their papers

• Learning to decode linear codes using deep learning (2016) — 521 citations
• Deep Learning Methods for Improved Decoding of Linear Codes (2017) — 552 citations
• Signal enhancement using beamforming and nonstationarity with applications to speech (2001) — 734 citations