Yoram Bachrach

Artificial Intelligence · 2009–2020

A researcher in the Science corpus: 7 papers; trusts 39 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 63 citations into it
• Reinforcement Learning — 31 citations into it
• Natural Language Processing — 24 citations into it
• Computer Vision and Pattern Recognition — 23 citations into it; #15,761 of 21,575 by trust here, #14,822 by citations
• Sociology and Political Science — 18 citations into it
• Management Science and Operations Research — 11 citations into it
• Information Retrieval & Search — 9 citations into it; #746 of 1,022 by trust here, #690 by citations
• Signal Processing — 9 citations into it; #5,406 of 7,572 by trust here, #4,201 by citations

Whom Yoram Bachrach trusts overall

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

1. Noam Koenigstein
2. Paul Resnick
3. Richard S. Sutton
4. Yehuda Koren
5. Alexandr Andoni
6. Geoffrey E. Hinton
7. Ilya Sutskever
8. Robert M. Bell
9. Yoshua Bengio
10. Nicholas I. Fisher

Who trusts Yoram Bachrach overall

1. Anshumali Shrivastava
2. Ping Li
3. Stephen O. Mussmann
4. Sohee Yang
5. Xueyuan Han
6. Ruiqi Guo
7. Firas Abuzaid
8. Rajarshi Das
9. Alex Auvolat
10. Minjoon Seo

Papers from Yoram Bachrach's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Open-ended Learning in Symmetric Zero-sum Games (2019) — 201 citations
• Speeding up the Xbox recommender system using a euclidean transformation for inner-product spaces (2014) — 166 citations
• Minimax Theorem for Latent Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets (2020) — 7 citations
• Personality and patterns of Facebook usage (2012) — 354 citations
• Studying User Income through Language, Behaviour and Affect in Social Media (2015) — 216 citations
• Batch Policy Gradient Methods for Improving Neural Conversation Models (2017) — 37 citations
• Sketching Techniques for Collaborative Filtering (2009) — 39 citations