Yftah Ziser

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 2 papers; trusts 66 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 32 citations into it; #8,002 of 35,345 by trust here, #17,412 by citations
• Representation & Self-Supervised Learning — 25 citations into it; #1,250 of 3,145 by trust here, #2,516 by citations
• Natural Language Processing — 17 citations into it; #2,087 of 5,688 by trust here, #4,361 by citations
• Computer Vision and Pattern Recognition — 10 citations into it

Whom Yftah Ziser trusts overall

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

1. Diederik P. Kingma
2. John Blitzer
3. Hal Daumé Iii
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Minmin Chen
7. Danilo Jimenez Rezende
8. Max Welling
9. Tomas Mikolov
10. Yann LeCun

Who trusts Yftah Ziser overall

1. Roi Reichart
2. Nadav Oved
3. Alan Ramponi
4. Eyal Ben-David
5. Guy Rotman
6. Xiaochuang Han
7. Jeremy Barnes
8. Rotem Dror
9. Amir Feder
10. Barbara Plank

Papers from Yftah Ziser's perspective

Ordered by how strongly the researchers Yftah Ziser 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. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Pivot Based Language Modeling for Improved Neural Domain Adaptation (2018) — 135 citations
• Neural Structural Correspondence Learning for Domain Adaptation (2016) — 111 citations