Noam Koenigstein

Artificial Intelligence · 2011–2019

A researcher in the Science corpus: 9 papers; trusts 56 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 50 citations into it; #8,977 of 35,345 by trust here, #17,412 by citations
• Information Systems — 37 citations into it; #900 of 8,771 by trust here, #1,815 by citations
• Natural Language Processing — 28 citations into it
• Computer Vision and Pattern Recognition — 24 citations into it; #8,273 of 21,575 by trust here, #11,721 by citations
• Recommender Systems — 23 citations into it; #79 of 233 by trust here, #188 by citations
• Representation & Self-Supervised Learning — 19 citations into it
• Information Retrieval & Search — 17 citations into it; #412 of 1,022 by trust here, #535 by citations
• Management Science and Operations Research — 12 citations into it

Whom Noam Koenigstein trusts overall

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

1. Yehuda Koren
2. Robert M. Bell
3. Gideon Dror
4. George Karypis
5. Radford M. Neal
6. S. Omohundro
7. Geoffrey E. Hinton
8. Ulrich Paquet
9. Ruslan Salakhutdinov
10. Oren Barkan

Who trusts Noam Koenigstein overall

1. Oren Barkan
2. Anshumali Shrivastava
3. Prem Gopalan
4. Firas Abuzaid
5. Yoram Bachrach
6. Ping Li
7. Parikshit Ram
8. Ulrich Paquet
9. Jake M. Hofman
10. Pedro G. Campos

Papers from Noam Koenigstein's perspective

Ordered by how strongly the researchers Noam Koenigstein 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Speeding up the Xbox recommender system using a euclidean transformation for inner-product spaces (2014) — 166 citations
• Efficient retrieval of recommendations in a matrix factorization framework (2012) — 96 citations
• One-class collaborative filtering with random graphs (2013) — 111 citations
• Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding (2019) — 38 citations
• Modelling Session Activity with Neural Embedding (2016) — 7 citations
• ITEM2VEC: Neural item embedding for collaborative filtering (2016) — 548 citations
• A Hybrid Explanations Framework for Collaborative Filtering Recommender Systems (2014) — 8 citations
• The Yahoo! Music Dataset and KDD-Cup '11 (2012) — 326 citations
• Recommendations : Modeling Music Ratings with Temporal Dynamics and Item Taxonomy (2011)