Bracha Shapira

Artificial Intelligence · 2001–2021

A researcher in the Science corpus: 7 papers; trusts 21 researchers and is trusted by 85 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 62 citations into it; #9,586 of 35,345 by trust here, #11,192 by citations
• Information Systems — 58 citations into it; #163 of 8,771 by trust here, #457 by citations
• Computer Vision and Pattern Recognition — 35 citations into it; #7,213 of 21,575 by trust here, #9,079 by citations
• Safety, Robustness & Interpretability — 22 citations into it
• Management Science and Operations Research — 18 citations into it; #9,207 of 11,946 by trust here, #9,383 by citations
• Computer Networks and Communications — 17 citations into it
• Recommender Systems — 16 citations into it; #29 of 233 by trust here, #75 by citations
• Signal Processing — 15 citations into it

Whom Bracha Shapira trusts overall

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

1. Robin D. Burke
2. J. Konstan
3. Ian T. Jolliffe
4. Gediminas Adomavicius
5. Scott M. Lundberg
6. Jerome H. Friedman
7. Geoffrey E. Hinton
8. J. Schafer
9. John Riedl
10. Jonathan L. Herlocker

Who trusts Bracha Shapira overall

1. Julian McAuley
2. Ruining He
3. Matevz Kunaver
4. Damianos Gavalas
5. Santosh Kabbur
6. Wang-Cheng Kang
7. Steven Euijong Whang
8. Xiwang Yang
9. Sergio Oramas
10. Jöran Beel

Papers from Bracha Shapira's perspective

Ordered by how strongly the researchers Bracha Shapira 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Reinforcement Learning: An Introduction
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet Large Scale Visual Recognition Challenge
10. ImageNet: A large-scale hierarchical image database

Their papers

• Recommender Systems: Introduction and Challenges (2015) — 884 citations
• Explaining anomalies detected by autoencoders using Shapley Additive Explanations (2021) — 451 citations
• RecSys Challenge 2015 and the YOOCHOOSE Dataset (2015) — 141 citations
• Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System (2015) — 313 citations
• Recommender Systems Handbook (2015) — 3,172 citations
• Introduction to Recommender Systems Handbook (2011) — 2,737 citations
• Information Filtering: Overview of Issues, Research and Systems (2001) — 462 citations